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Senior Machine Learning Scientist🌍 UK or Poland (Remote or Hybrid, it’s up to you!)💰 Dependent on experience📈 Be part of our success with the opportunity to join our company equity schemeOur Perks  ❤️ Monthly Health & Wellness budget, increasing with length of service📚 Annual Learning and Development budget, increasing with length of service🤸🏽‍♂️ Flexible working in a choice-first environment - we trust the way you want to work!🖥️ Work From Home Allowance🌴 25 Holiday Days + your local bank holidays, plus an extra day for every year of service🎂 Your birthday off🍼 Enhanced Family Leave (UK Only), Fertility Leave, and Neonatal Leave⚕️ Optional Healthcare Plan🛟 Life & income protection (Location dependent)🤝 Employee Assistance Programme (UK Only)📈 The opportunity to share in the company’s success through options🌆 If you’re in London, a dog-friendly office with great classes, events, and a rooftop terrace 🦸‍♀️ The Role 🦸‍♀️Our mission is to help large successful brands like Uber, Amazon, Wise, HelloFresh (and more!) put their customers at the centre of everything they do. Using best-in-class tech in a fast-developing AI space, our Customer Experience Intelligence platform continuously analyses explicit and implicit feedback to enable our clients to identify what they should do next.We're hiring a Senior Machine Learning Scientist to join the team and help build and ship the next generation of that stack. 👉 What you'll be doing:Unlike many companies, we use our own custom models, specialised for customer feedback, across various parts of the stack: extraction, retrieval, reranking, summarisation, and sentiment analysis. We are also pragmatic and understand that the right solution can be a combination of off-the-shelf LLMs, bespoke fine-tuned models, and sometimes techniques that utilise no LLM at all.This means you will:Train, evaluate, and iterate on ML models and agentic systems for customer feedback, including owning our custom fine-tuning pipelines. Run experiments end-to-end, track results rigorously, and make clear recommendations on what to ship, iterate, or retire.Build and maintain LLM-powered features: retrieval pipelines, reranking systems, insight agents, data mining agents, and automated taxonomy generation.Design and run robust evaluation frameworks: build test sets, define metrics, evaluate non-deterministic systems, handle class imbalance, and automate checkpoint comparisons.Improve and extend semantic search and retrieval, evolving from embedding-based approaches toward more advanced methods.Write production-quality code and collaborate closely with Engineering on productionisation, model serving, data pipelines, and monitoring.Work with Product and Commercial teams to translate business needs into practical ML solutions, and support client evaluations and accuracy benchmarking.Mentor team members, review code and research, and bring relevant advances from the literature into the product.🧰 What you’ll need: A deep working knowledge of transformer architectures.Strong PyTorch skills, with the ability to write custom training loops, modify model architectures, and debug issues at the tensor level. Ideally, experience with parameter-efficient fine-tuning techniques such as LoRAExtensive experience working with large-scale, messy real-world text data, including classification, extraction, embeddings, re-rankers, clustering, and search.Experience in instruction fine-tuning and serving language models, familiarity with frameworks such as vLLM, DeepSpeed, or similar toolsA solid grounding in classical ML and statistics, and the judgement to choose simpler methods when they’re the right solution.Practical experience building with GenAI and agentic patterns.Excellent communication skills and confidence translating complex technical concepts for non-technical audiences (and vice versa!).Technical curiosity and a keen interest in AI – a love of experimenting to make the most of available technology.High ownership and initiative, with the ability to identify problems, prioritise effectively, and drive solutions forward.➕It would be a bonus if you:MSc/PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Computational Linguistics or a closely related STEM field.Experience with reinforcement learning techniques, such as with verifiable reward (RLVR) 🔎 Our Hiring ProcessLet’s introduce ourselves – you’ll complete an introductory asynchronous interview - we’d love to learn more about you, your ambitions, and what you’re looking for in your next step.Get to know your would-be manager – you’ll have a call with Aji, our Chief Scientist, to learn more about the role and show off your experience.Show us how you work – you'll complete a short take home assignmentGet to know your would-be team – You'll meet a mix of people who you'll be working closely with from the Data Science, Engineering and Product teams.How our values and your career goals align – you’ll have a call with our cofounder to learn more about life at Chattermill and ensure we’re the right place for your next stage of growth. 💖 Our Values We are obsessed with experience – We take our mission to rid the world of bad Customer Experience seriously, and we practice what we preach.We believe in the power of trust – Whether it's with each other, our customers, partners, or other stakeholders, we always communicate with openness and trust.We act as responsible owners – Whether it's about the company, a team, a project, or a task, having the freedom to make decisions in our area of responsibility is a crucial driver for us.We share a passion for growth & progress – On every level, we’re motivated by taking on new challenges – even if they seem out of reach. We recognise that we are learning machines and we always seek to action feedback and improve collectively.We set our ambitions high but stay humble – We've come together to build a product and a category that’s never been seen before. While we're an ambitious bunch with lofty goals, we don't approach this goal carelessly.We believe the right team is the key to success – At Chattermill we’ve learned that all our important achievements have been the result of the right people collaborating together – that’s why we need you to apply today! 🌈 Diversity & Inclusion 🌈We want to enable exceptional experiences for everyone, and to achieve this we need everyone’s voice in our team.  We are on a mission to bring more diversity into the business and to give everyone (from all backgrounds and abilities) a chance to join us, even if they may not fit all of the requirements set out in this job spec. We realise that some may be hesitant to apply for a role when they don’t meet 100% of the listed requirements – we believe in potential and will happily consider all applications based on the skills and experience you have, we’d love to be part of your growth and we encourage you to apply!   #machinelearning #machinelearningscientist #seniormachinelearningscientist #datascience #deeplearning #LLM #ML #data #AI #PyTorch

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About UsHarmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.About the RoleWe are looking for a Machine Learning Engineer to join our Semantic Scene Understanding team in Paris. In this role, you will design the core algorithms to extract semantic information in real-time from the theatre of operations as seen through the different cameras of our different UAVs, to improve the operator’s scene understanding.ResponsibilitiesDesign and Train: Develop state-of-the-art machine learning algorithms for semantic segmentation, object detection, and classification tailored to aerial imagery.Advanced Feature Extraction: Build high-level tactical features on top of base semantic data, such as real-time road vectorization, trafficability analysis, and dynamic obstacle mapping.Multi-Agent Fusion: Architect pipelines that temporally and spatially align semantic data from multiple moving UAVs into a cohesive Common Operational Picture (COP).Edge Optimization: Optimize and deploy these algorithms directly into our tactical C2 platform, utilizing quantization, pruning, and hardware acceleration to meet strict real-time compute constraints.Candidate RequirementsEducational Background: MSc in Computer Science, Machine Learning, or a related field. A PhD is a strong plus.Foundational Knowledge: Deep understanding of Machine Learning theory, Linear Algebra, and 3D-Geometry algorithms.Core Tech Stack: Expert-level command of Python and deep learning frameworks (PyTorch).Performance Engineering: Experience with C++ and inference optimization frameworks (e.g., TensorRT, ONNX Runtime, CUDA) is highly desirable.Domain Experience (Plus): A track record of shipping CV/ML algorithms in production, particularly for edge/embedded systems or involving aerial (EO/IR) imagery.Strong Ownership: Ability to take a feature from an ArXiv paper all the way to a ruggedized tactical PC.Adaptability & Mission Focus: Thrives in a fast-paced startup environment and is 100% dedicated to building ethical defense technologies that bring a strategic edge to allied nations.Communication: Excellent verbal and written communication skills to collaborate effectively with software engineers and hardware teams.We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.

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About the Team: Our Safety Systems org ensures that OpenAI’s most capable models can be responsibly developed and deployed. We build evaluations, safeguards, and safety frameworks that help our models behave as intended in real-world settings. About the Role:We are seeking a Researcher in Privacy-Preserving Safety to help design and build the next generation of privacy-preserving safety systems for frontier AI models. This role sits at the intersection of AI safety, security, and privacy, with a focus on developing auditable, privacy-first mechanisms that enable robust harm detection and mitigation without exposing sensitive user data.You will help define and operationalize frameworks for identifying and addressing frontier risks (e.g., bioweapon instructions, malware creation, suicide/self-harm risks, jailbreaks), while ensuring that privacy guarantees remain intact—even under adversarial conditions.This role is central to our long-term goal of scaling our automated privacy-preserving safety systems to mitigate potential harms while minimizing human review.You’ll work on foundational problems such as privacy-preserving monitoring, algorithmic auditing, secure enclaves, and adversarially robust safety enforcement protocols, helping ensure that safety systems scale without compromising user trust.In this role, you will:Design and implement privacy-first architectures for detecting and mitigating harmful model behaviors.Build frameworks for auditable private identification of high-risk content (jailbreaks, cyber threats, or weaponization instructions).Develop strict, auditable mechanisms triggered only by harm signals.Drive the development of automated safety systems that preserve privacy at every level. You might thrive in this role if you:Are a researcher with deep interest in privacy, security, and AI safety, motivated by building systems that are both trustworthy and effective at scale.Hold a PhD or equivalent experience in Computer Science, Cryptography, Security, Machine Learning, or related fieldsHave the ability to translate ambiguous problem spaces into formal frameworks and deployable systemsDemonstrate profiency in one or more of:Privacy-preserving computation (e.g., secure enclaves, MPC, differential privacy)Security and adversarial systemsMachine learning safety or alignmentExperience designing robust systems under adversarial threat modelsHave experience with AI safety, jailbreak detection, or model alignmentAre familiar with privacy-preserving machine learning techniques, algorithmic auditing and/or secure system designAbout OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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About the teamOpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development.About the roleForward Deployed Engineers (FDEs) lead complex end-to-end deployments of frontier models in production alongside our most strategic customers. You will own discovery, technical scoping, system design, build, and production rollout, partnering directly with customer engineering and domain teams.You will measure success through production adoption, measurable workflow impact, and eval-driven feedback that changes product and model roadmaps. You’ll work closely with our Product, Research, Partnerships, GRC, Security, and GTM teams.This role is based in Sydney. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required.In this role you willOwn technical delivery across multiple deployments from first prototype to stable production.Build full-stack systems that deliver customer value and sharpen how we learn.Embed closely with customer teams, understand their needs, and guide adoption of what you build.Scope work, sequence delivery, and remove blockers early.Make trade-offs between scope, speed, and quality; adjust plans to protect delivery.Contribute directly in the code when progress or clarity depends on it.Codify working patterns into tools, playbooks, or building blocks that others can use.Share field feedback that helps Research and Product understand where the models succeed and where they can improve.Keep teams moving through clarity and follow-through.You might thrive in this role if youBring 5+ years of engineering or technical deployment experience that includes customer-facing work.Have scoped and delivered complex systems in fast-moving or ambiguous environments.Write and review production-grade code across frontend and backend using Python, JavaScript, or comparable stacks.Have built or deployed systems powered by LLMs or generative models and understand how model behaviour affects product experience.Simplify complexity and make fast, sound decisions under pressure.Communicate clearly with engineers, product teams, and customer stakeholders.Spot risks early and adjust without slowing down.Model calm and judgment when the stakes are high.About OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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Forward Deployed AI EngineerThe opportunityWe are looking for a Forward Deployed AI Engineer to serve as the critical bridge between Latent Labs’ frontier generative models and the customers who rely on them. You will work directly with pharmaceutical and biotech customers to deploy, integrate and optimise our technology within their scientific workflows. This is a highly technical, customer-facing role that combines deep infrastructure expertise with a passion for solving real-world problems in drug discovery and protein engineering.You will work closely with our customers, understanding their unique technical environments and ensuring that our generative biology platform integrates seamlessly with their systems. You will own the full lifecycle of customer deployments - from initial technical scoping through to production-grade delivery - and act as the voice of the customer back into our product and research teams.Who we areAt Latent Labs, we are building frontier models that learn the fundamentals of biology. We pursue ambitious goals with curiosity and are committed to scientific excellence. Before building Latent Labs, our team co-developed DeepMind’s Nobel-prize winning AlphaFold, invented latent diffusion, and built pioneering lab data management systems as well as high throughput protein screening platforms. At Latent Labs you will be working with some of the brightest minds in generative AI and biology.Our team is committed to interdisciplinary exchange, continuous learning and collaboration. Team offsites help us foster a culture of trust across our London and San Francisco sites.We’re looking for innovators passionate about tackling complex challenges and maximizing positive global impact. Join us on our moonshot mission.Who you areYou have a strong CS or ML educational background. You hold a degree (BSc, MSc or PhD) in Computer Science, Machine Learning, or a closely related quantitative field. You have a solid grounding in software engineering principles and modern ML frameworks.You have built systems that access large models via APIs. You have significant experience designing, deploying and maintaining infrastructure for large-scale model serving and have hands-on experience building robust API layers around ML models.You are customer-facing and delivery-oriented. You have direct experience deploying AI systems for external customers. You can translate complex technical concepts into clear language for non-technical stakeholders and thrive in environments where customer success is the primary measure of your work.You are fluent in cloud infrastructure. You have hands-on experience with AWS and ideally other major cloud platforms (GCP, Azure). You are comfortable with containerisation (Docker, Kubernetes), CI/CD pipelines, and cloud-native architectures.You are a strong communicator and collaborator. You work effectively across functions - with research scientists and business executives alike. You are comfortable leading technical discussions, writing clear documentation, and presenting solutions to senior stakeholders at partner organisations.You are mission driven and adaptable. You are passionate about making a positive impact on the world, whether it’s for patients, customers or beyond. You thrive in a dynamic, fast-paced environment where priorities can shift and you need to context-switch between multiple customer engagements.What sets you apartYou have experience with bio or protein design models. You have worked on ML-driven projects in computational biology, protein design, or related life science domains. You understand the unique data challenges and evaluation paradigms of biological modelling.You have contributed to generative modelling innovation. You have a track record of novel contributions to generative modelling - whether through publications, open-source work, or impactful product features.You have built production enterprise software. You have experience delivering software that meets enterprise-grade requirements for security, compliance, auditability and uptime. You understand the difference between a prototype and a production system.You have pharma or biotech industry experience. You understand the regulatory landscape, data governance requirements and scientific workflows common in pharmaceutical and biotech organisations.Your responsibilitiesCustomer deployment & integration:Drive the end-to-end technical deployment of Latent Labs models into customer environments, ensuring seamless integration with existing scientific and IT infrastructure.Design and build production-grade API integrations, data pipelines and model-serving infrastructure tailored to each customer’s requirements.Work on-site or embedded with pharma and biotech partners to scope technical requirements, troubleshoot issues and deliver solutions.Ensure deployments meet enterprise standards for security, performance and reliability.Customer advocacy & product feedback:Serve as the technical point of contact for assigned customers, building trusted relationships with their scientific and engineering teams, including spending time working on-site at international partner locations as neededGather and synthesise customer feedback, translating it into actionable insights for our product, research and platform teams.Collaborate with internal teams to shape the product roadmap based on real-world deployment learnings.Create technical documentation, integration guides and best-practice resources for customers.Self development:Stay on top of the latest developments in ML infrastructure, model serving and cloud-native tooling.Gain a strong working understanding of protein and cell biology as it relates to our product.Participate in knowledge sharing, e.g. organise and present at our internal reading group.ApplyWe offer strongly competitive compensation and benefits packages, including:Private health insurancePension contributionsGenerous leave policies (including gender neutral parental leave)Hybrid workingTravel opportunities and moreWe also offer a stimulating work environment, and the opportunity to shape the future of synthetic biology through the application of breakthrough generative models.We welcome applicants from all backgrounds and we are committed to building a team that represents a variety of backgrounds, perspectives, and skills.

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Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities.Tenstorrent is seeking an Physical Design Engineer to lead  cross-functional efforts to solve complex physical design challenges and develop end-to-end RTL-to-GDS methodologies across advanced nodes, with a strong focus on PPA and runtime improvements. The engineer will architect, integrate, and deploy AI/ML-driven solutions into production physical design flows, creating custom CAD tools and partnering with internal teams and EDA vendors to drive next-generation, ML-enabled capabilities.  This role is hybrid, based out of Santa Clara, CA or Austin, TX or Fort Collins, CO. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.   Who you are BS in Electrical or Computer Engineering (or equivalent experience) with 5+ years in Physical Design CAD methodology at advanced nodes. Proven track record improving PPA and/or runtime on high-performance, low-power taped-out designs. Hands-on with industry-standard EDA tools (e.g., Fusion Compiler) across synthesis, P&R, STA, signoff, and hierarchical flows. Strong Python/Tcl and data skills, with interest or experience in ML frameworks (PyTorch, TensorFlow), and the ability to drive complex projects independently.   What we need Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, P&R, STA, signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and QoR. Optimize EDA tools and custom CAD flows using data-driven and ML-based techniques, in close collaboration with verification, extraction, timing, DFT, and EDA vendors.   What you will learn How to scale AI/ML-driven methodologies across diverse products and advanced technology nodes in real production flows. New ways to blend classical EDA algorithms with modern ML techniques to push PPA and runtime limits. Best practices for deploying, validating, and monitoring ML models in production CAD environments. How to influence next-generation ML-enabled EDA tools and collaborate deeply with cross-functional teams (PV, extraction, timing, DFT).   Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made. Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer. This position requires access to technology that requires a U.S. export license for persons whose most recent country of citizenship or permanent residence is a U.S. EAR Country Groups D:1, E1, or E2 country. This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology.  Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2).   These requirements apply to persons located in the U.S. and all countries outside the U.S.  As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency.  If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.

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Gong harnesses the power of AI to transform how revenue teams win. The Gong Revenue AI Operating System unifies data, insights, and workflows into a single, trusted system that observes, guides, and acts alongside the world’s most successful revenue teams. Powered by the Gong Revenue Graph, AI-powered intelligence, specialized agents, and trusted applications, Gong helps more than 5,000 companies around the world deeply understand their teams and customers, automate critical sales workflows, and close more deals with less effort. For more information, visit www.gong.io. At Gong, you will join a company built on innovative products, ambitious goals, and passionate people. We are shaping the future of revenue intelligence and we want people who are excited to build what comes next. You will work with a team that dreams big, moves fast, and cares deeply about the craft and about each other. Here, transparency and trust are core to how we operate, and every person has the opportunity to make a visible impact. If you want to grow, stretch, and do work that truly matters, Gong is the place to do the best work of your career.Gong is seeking a hands-on Staff, AI Enablement and Innovation professional to own our internal AI operating model. Sitting within our IT organization, this role is the heartbeat of our internal digital transformation. You will empower our internal teams by bridging the gap between high-level business discovery and deep technical execution. You will be the primary architect of Gong’s internal agentic strategy—responsible for "mining" the business for efficiency opportunities while simultaneously building the centralized orchestration layer that ensures our enterprise AI spend is governed, consistent, and scalable. This is a high-impact IC (Individual Contributor) role designed for a "scrappy builder" who thrives on turning internal complexity into streamlined, automated excellence. RESPONSIBILITIES Strategy & Governance (The "Guardrails") Define the Roadmap: Partner with Security, Legal, and business leaders to define the internal AI roadmap. Own the Stack: Operate the enterprise AI stack, including LLMs, vector databases, and gateways. Standardization: Enforce consistent patterns for tool calling, prompt versioning, state management, and error handling to prevent fragmented, "ad-hoc" agent implementations. Lifecycle Management: Manage the full model lifecycle, from evaluation and testing to upgrades and deprecations. Discovery & Execution (The "Gold Mining") Business Partnership: Proactively interview teams (Talent, Support, Sales) to identify manual workflows that can be automated via agentic AI. Proof of Efficacy: Build and deploy POCs independently to demonstrate ROI before scaling. Financial & Performance Operations (The "Numbers") Cost Management: Own the token procurement process and build forecasting/chargeback models to prevent uncontrolled spend. Performance Monitoring: Build dashboards to track SLAs/SLOs (latency, accuracy, uptime) and monitor usage, cost, and error rates. Optimization: Proactively identify opportunities for cost-saving (e.g., model switching) and performance tuning. QUALIFICATIONS The Persona: You are a Senior IT Business Analyst, Technical Implementation Lead, or Solutions Architect.  Technical Depth: Practical, hands-on experience with the modern AI stack (OpenAI, Gemini, Anthropic, Vector DBs). You understand the nuances of state management and prompt versioning. Scrappy Builder: You have a "hands-on-keyboard" mentality. You can take an idea from a stakeholder and turn it into a working agentic workflow without needing external engineering resources. Business Acumen: Ability to translate complex technical AI patterns into clear business value and ROI for stakeholders. Operational Rigor: Experience managing vendor relationships, forecasting technical costs (tokens), and maintaining system uptime/SLAs. YOU ARE Orchestration: Experienced with LangChain, or similar agentic frameworks. AI Tooling: Prompt Flow, Vector Databases, and API integration. Data & Analytics: Ability to build performance and cost-tracking dashboards (SQL, Tableau, etc.). PERKS & BENEFITS  We offer Gongsters a variety of medical, dental, and vision plans, designed to fit you and your family’s needs. Wellbeing Fund - flexible wellness stipend to support a healthy lifestyle. Mental Health benefits with covered therapy and coaching. 401(k) program to help you invest in your future. Education & learning stipend for personal growth and development. Flexible vacation time to promote a healthy work-life blend. Paid parental leave to support you and your family. Company-wide recharge days each quarter. Work from home stipend to help you succeed in a remote environment. The annual salary hiring range for this position is $148,000 - $225,000 USD.  Compensation is based on factors unique to each candidate, including, but not limited to, job-related skills, qualification, education, experience, and location. At Gong, we have a location-based compensation structure, which means there may be a different range for candidates in other locations. The total compensation package for this position, in addition to base compensation, may include incentive compensation, bonus, equity, and benefits. Some of our sales compensation programs also offer the potential to achieve above targeted earnings for those who exceed their sales targets.  We are always looking for outstanding Gongsters! So if this sounds like something that interests you regardless of compensation, please reach out. We may have more roles for you to consider and would love to connect. We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Gong recruiting email communications will always come from the @gong.io domain. Any outreach claiming to be from Gong via other sources should be ignored. Gong is an equal-opportunity employer. We believe that diversity is integral to our success, and do not discriminate based on race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, military status, genetic information, or any other basis protected by applicable law. To review Gong's privacy policy, visit https://www.gong.io/gong-io-job-candidates-privacy-notice/ for more details.

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Founded in 2015, Shield AI is a venture-backed deep-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include the V-BAT and X-BAT aircraft, Hivemind Enterprise, and the Hivemind Vision product lines. With offices and facilities across the U.S., Europe, the Middle East, and the Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. Job Description: Founded in 2015, Shield AI is a venture-backed defense technology company whose mission is to protect service members and civilians with intelligent systems. In pursuit of this mission, Shield AI is building the world’s best AI pilot. Its AI pilot, Hivemind, has flown a fighter jet (F-16), a vertical takeoff and landing drone (V-BAT), and a quadcopter (Nova). The company has offices in San Diego, Dallas, Washington DC and abroad. Shield AI’s products and people are currently in the field actively supporting operations with the U.S. Department of Defense and U.S. allies. As a Guidance and Controls engineer, you will be responsible creating and maintaining all of control and autonomy algorithms within the XBAT code base. This includes algorithm development, unit tests, component tests, flight software qualification and flight test support. You will also be responsible for helping update and validate the truth models as required.Required qualifications: Typically requires a minimum of 7 years of related experience with a Bachelor’s degree; or 3 years and a Master’s degree; or a PhD with 2 year experience; or equivalent experience. Proven track record of successfully shipping products, showcasing the ability to navigate through development cycles, overcome obstacles, and deliver high-quality solutions to meet project deadlines and exceed expectations in a fast-paced environment.. Excellent problem-solving and analytical skills, with a focus on delivering user-centric software solutions. Preferred qualifications: Familiarity with continuous integration / delivery and test-driven development Experience working with robotics and/or control systems, specifically unmanned aerial systems 180,000 - 280,000 a year#LI-SM1 #LD Full-time regular employee offer package: Pay within range listed + Bonus + Benefits + Equity Temporary employee offer package: Pay within range listed above + temporary benefits package (applicable after 60 days of employment) Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information. ### Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. 

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About the Role The Turbo team sits at the intersection of efficient inference (algorithms, architectures, engines) and post‑training / RL systems. We build and operate the systems behind Together’s API, including high‑performance inference and RL/post‑training engines that can run at production scale. Our mandate is to push the frontier of efficient inference and RL‑driven training: making models dramatically faster and cheaper to run, while improving their capabilities through RL‑based post‑training (e.g., GRPO‑style objectives). This work lives at the interface of algorithms and systems: asynchronous RL, rollout collection, scheduling, and batching all interact with engine design, creating many knobs to tune across the RL algorithm, training loop, and inference stack. Much of the job is modifying production inference systems—for example, SGLang‑ or vLLM‑style serving stacks and speculative decoding systems such as ATLAS—grounded in a strong understanding of post‑training and inference theory, rather than purely theoretical algorithm design. You’ll work across the stack—from RL algorithms and training engines to kernels and serving systems—to build and improve frontier models via RL pipelines. People on this team are often spiky: some are more RL‑first, some are more systems‑first. Depth in one of these areas plus appetite to collaborate across (and grow toward more full‑stack ownership over time) is ideal. Requirements We don’t expect anyone to check every box below. People on this team typically have deep expertise in one or more areas and enough breadth (or interest) to work effectively across the stack. The closer you are to full‑stack (inference + post‑training/RL + systems), the stronger the fit—but being spiky in one area and eager to grow is absolutely okay. You might be a good fit if you: Have strong expertise in at least one of the following, and are excited to collaborate across (and grow into) the others: Systems‑first profile: Large‑scale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving. RL‑first profile: RL / post‑training for LLMs or large models (e.g., GRPO, RLHF/RLAIF, DPO‑like methods, reward modeling), and using these to train or fine‑tune real models. Model architecture design for Transformers or other large neural nets. Distributed systems / high‑performance computing for ML. Are comfortable working from algorithms to engines: Strong coding ability in Python Experience profiling and optimizing performance across GPU, networking, and memory layers. Able to take a new sampling method, scheduler, or RL update and turn it into a production‑grade implementation in the engine and/or training stack. Have a solid research foundation in your area(s) of depth: Track record of impactful work in ML systems, RL, or large‑scale model training (papers, open‑source projects, or production systems). Can read new RL / post‑training papers, understand their implications on the stack, and design minimal, correct changes in the right layer (training engine vs. inference engine vs. data / API). Operate well as a full‑stack problem solver: You naturally ask: “Where in the stack is this really bottlenecked?” You enjoy collaborating with infra, research, and product teams, and you care about both scientific quality and user‑visible wins. Minimum qualifications 3+ years of experience working on ML systems, large‑scale model training, inference, or adjacent areas (or equivalent experience via research / open source). Advanced degree in Computer Science, EE, or a related field, or equivalent practical experience. Demonstrated experience owning complex technical projects end‑to‑end. If you’re excited about the role and strong in some of these areas, we encourage you to apply even if you don’t meet every single requirement. Responsibilities Advance inference efficiency end‑to‑end Design and prototype algorithms, architectures, and scheduling strategies for low‑latency, high‑throughput inference. Implement and maintain changes in high‑performance inference engines (e.g., SGLang‑ or vLLM‑style systems and Together’s inference stack), including kernel backends, speculative decoding (e.g., ATLAS), quantization, etc. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Unify inference with RL / post‑training Design and operate RL and post‑training pipelines (e.g., RLHF, RLAIF, GRPO, DPO‑style methods, reward modeling) where 90+% of the cost is inference, jointly optimizing algorithms and systems. Make RL and post‑training workloads more efficient with inference‑aware training loops—for example, async RL rollouts, speculative decoding, and other techniques that make large‑scale rollout collection and evaluation cheaper. Use these pipelines to train, evaluate, and iterate on frontier models on top of our inference stack. Co‑design algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, and quickly identify bottlenecks across the training engine, inference engine, data pipeline, and user‑facing layers. Run ablations and scale‑up experiments to understand trade‑offs between model quality, latency, throughput, and cost, and feed these insights back into model, RL, and system design. Own critical systems at production scale Profile, debug, and optimize inference and post‑training services under real production workloads. Drive roadmap items that require real engine modification—changing kernels, memory layouts, scheduling logic, and APIs as needed. Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Provide technical leadership (Staff level) Set technical direction for cross‑team efforts at the intersection of inference, RL, and post‑training. Mentor other engineers and researchers on full‑stack ML systems work and performance engineering. About Together AI Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $280,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy    

false

About the Role The Turbo team sits at the intersection of efficient inference (algorithms, architectures, engines) and post‑training / RL systems. We build and operate the systems behind Together’s API, including high‑performance inference and RL/post‑training engines that can run at production scale. Our mandate is to push the frontier of efficient inference and RL‑driven training: making models dramatically faster and cheaper to run, while improving their capabilities through RL‑based post‑training (e.g., GRPO‑style objectives). This work lives at the interface of algorithms and systems: asynchronous RL, rollout collection, scheduling, and batching all interact with engine design, creating many knobs to tune across the RL algorithm, training loop, and inference stack. Much of the job is modifying production inference systems—for example, SGLang‑ or vLLM‑style serving stacks and speculative decoding systems such as ATLAS—grounded in a strong understanding of post‑training and inference theory, rather than purely theoretical algorithm design. You’ll work across the stack—from RL algorithms and training engines to kernels and serving systems—to build and improve frontier models via RL pipelines. People on this team are often spiky: some are more RL‑first, some are more systems‑first. Depth in one of these areas plus appetite to collaborate across (and grow toward more full‑stack ownership over time) is ideal. Requirements We don’t expect anyone to check every box below. People on this team typically have deep expertise in one or more areas and enough breadth (or interest) to work effectively across the stack. The closer you are to full‑stack (inference + post‑training/RL + systems), the stronger the fit—but being spiky in one area and eager to grow is absolutely okay. You might be a good fit if you: Have strong expertise in at least one of the following, and are excited to collaborate across (and grow into) the others: Systems‑first profile: Large‑scale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving. RL‑first profile: RL / post‑training for LLMs or large models (e.g., GRPO, RLHF/RLAIF, DPO‑like methods, reward modeling), and using these to train or fine‑tune real models. Model architecture design for Transformers or other large neural nets. Distributed systems / high‑performance computing for ML. Are comfortable working from algorithms to engines: Strong coding ability in Python Experience profiling and optimizing performance across GPU, networking, and memory layers. Able to take a new sampling method, scheduler, or RL update and turn it into a production‑grade implementation in the engine and/or training stack. Have a solid research foundation in your area(s) of depth: Track record of impactful work in ML systems, RL, or large‑scale model training (papers, open‑source projects, or production systems). Can read new RL / post‑training papers, understand their implications on the stack, and design minimal, correct changes in the right layer (training engine vs. inference engine vs. data / API). Operate well as a full‑stack problem solver: You naturally ask: “Where in the stack is this really bottlenecked?” You enjoy collaborating with infra, research, and product teams, and you care about both scientific quality and user‑visible wins. Minimum qualifications 3+ years of experience working on ML systems, large‑scale model training, inference, or adjacent areas (or equivalent experience via research / open source). Advanced degree in Computer Science, EE, or a related field, or equivalent practical experience. Demonstrated experience owning complex technical projects end‑to‑end. If you’re excited about the role and strong in some of these areas, we encourage you to apply even if you don’t meet every single requirement. Responsibilities Advance inference efficiency end‑to‑end Design and prototype algorithms, architectures, and scheduling strategies for low‑latency, high‑throughput inference. Implement and maintain changes in high‑performance inference engines (e.g., SGLang‑ or vLLM‑style systems and Together’s inference stack), including kernel backends, speculative decoding (e.g., ATLAS), quantization, etc. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Unify inference with RL / post‑training Design and operate RL and post‑training pipelines (e.g., RLHF, RLAIF, GRPO, DPO‑style methods, reward modeling) where 90+% of the cost is inference, jointly optimizing algorithms and systems. Make RL and post‑training workloads more efficient with inference‑aware training loops—for example, async RL rollouts, speculative decoding, and other techniques that make large‑scale rollout collection and evaluation cheaper. Use these pipelines to train, evaluate, and iterate on frontier models on top of our inference stack. Co‑design algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, and quickly identify bottlenecks across the training engine, inference engine, data pipeline, and user‑facing layers. Run ablations and scale‑up experiments to understand trade‑offs between model quality, latency, throughput, and cost, and feed these insights back into model, RL, and system design. Own critical systems at production scale Profile, debug, and optimize inference and post‑training services under real production workloads. Drive roadmap items that require real engine modification—changing kernels, memory layouts, scheduling logic, and APIs as needed. Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Provide technical leadership (Staff level) Set technical direction for cross‑team efforts at the intersection of inference, RL, and post‑training. Mentor other engineers and researchers on full‑stack ML systems work and performance engineering. About Together AI Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $280,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy    

false

About the Role The Turbo team sits at the intersection of efficient inference (algorithms, architectures, engines) and post‑training / RL systems. We build and operate the systems behind Together’s API, including high‑performance inference and RL/post‑training engines that can run at production scale. Our mandate is to push the frontier of efficient inference and RL‑driven training: making models dramatically faster and cheaper to run, while improving their capabilities through RL‑based post‑training (e.g., GRPO‑style objectives). This work lives at the interface of algorithms and systems: asynchronous RL, rollout collection, scheduling, and batching all interact with engine design, creating many knobs to tune across the RL algorithm, training loop, and inference stack. Much of the job is modifying production inference systems—for example, SGLang‑ or vLLM‑style serving stacks and speculative decoding systems such as ATLAS—grounded in a strong understanding of post‑training and inference theory, rather than purely theoretical algorithm design. You’ll work across the stack—from RL algorithms and training engines to kernels and serving systems—to build and improve frontier models via RL pipelines. People on this team are often spiky: some are more RL‑first, some are more systems‑first. Depth in one of these areas plus appetite to collaborate across (and grow toward more full‑stack ownership over time) is ideal. Requirements We don’t expect anyone to check every box below. People on this team typically have deep expertise in one or more areas and enough breadth (or interest) to work effectively across the stack. The closer you are to full‑stack (inference + post‑training/RL + systems), the stronger the fit—but being spiky in one area and eager to grow is absolutely okay. You might be a good fit if you: Have strong expertise in at least one of the following, and are excited to collaborate across (and grow into) the others: Systems‑first profile: Large‑scale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving. RL‑first profile: RL / post‑training for LLMs or large models (e.g., GRPO, RLHF/RLAIF, DPO‑like methods, reward modeling), and using these to train or fine‑tune real models. Model architecture design for Transformers or other large neural nets. Distributed systems / high‑performance computing for ML. Are comfortable working from algorithms to engines: Strong coding ability in Python Experience profiling and optimizing performance across GPU, networking, and memory layers. Able to take a new sampling method, scheduler, or RL update and turn it into a production‑grade implementation in the engine and/or training stack. Have a solid research foundation in your area(s) of depth: Track record of impactful work in ML systems, RL, or large‑scale model training (papers, open‑source projects, or production systems). Can read new RL / post‑training papers, understand their implications on the stack, and design minimal, correct changes in the right layer (training engine vs. inference engine vs. data / API). Operate well as a full‑stack problem solver: You naturally ask: “Where in the stack is this really bottlenecked?” You enjoy collaborating with infra, research, and product teams, and you care about both scientific quality and user‑visible wins. Minimum qualifications 3+ years of experience working on ML systems, large‑scale model training, inference, or adjacent areas (or equivalent experience via research / open source). Advanced degree in Computer Science, EE, or a related field, or equivalent practical experience. Demonstrated experience owning complex technical projects end‑to‑end. If you’re excited about the role and strong in some of these areas, we encourage you to apply even if you don’t meet every single requirement. Responsibilities Advance inference efficiency end‑to‑end Design and prototype algorithms, architectures, and scheduling strategies for low‑latency, high‑throughput inference. Implement and maintain changes in high‑performance inference engines (e.g., SGLang‑ or vLLM‑style systems and Together’s inference stack), including kernel backends, speculative decoding (e.g., ATLAS), quantization, etc. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Unify inference with RL / post‑training Design and operate RL and post‑training pipelines (e.g., RLHF, RLAIF, GRPO, DPO‑style methods, reward modeling) where 90+% of the cost is inference, jointly optimizing algorithms and systems. Make RL and post‑training workloads more efficient with inference‑aware training loops—for example, async RL rollouts, speculative decoding, and other techniques that make large‑scale rollout collection and evaluation cheaper. Use these pipelines to train, evaluate, and iterate on frontier models on top of our inference stack. Co‑design algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, and quickly identify bottlenecks across the training engine, inference engine, data pipeline, and user‑facing layers. Run ablations and scale‑up experiments to understand trade‑offs between model quality, latency, throughput, and cost, and feed these insights back into model, RL, and system design. Own critical systems at production scale Profile, debug, and optimize inference and post‑training services under real production workloads. Drive roadmap items that require real engine modification—changing kernels, memory layouts, scheduling logic, and APIs as needed. Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Provide technical leadership (Staff level) Set technical direction for cross‑team efforts at the intersection of inference, RL, and post‑training. Mentor other engineers and researchers on full‑stack ML systems work and performance engineering. About Together AI Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $280,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy    

false

About the Role The Turbo team sits at the intersection of efficient inference (algorithms, architectures, engines) and post‑training / RL systems. We build and operate the systems behind Together’s API, including high‑performance inference and RL/post‑training engines that can run at production scale. Our mandate is to push the frontier of efficient inference and RL‑driven training: making models dramatically faster and cheaper to run, while improving their capabilities through RL‑based post‑training (e.g., GRPO‑style objectives). This work lives at the interface of algorithms and systems: asynchronous RL, rollout collection, scheduling, and batching all interact with engine design, creating many knobs to tune across the RL algorithm, training loop, and inference stack. Much of the job is modifying production inference systems—for example, SGLang‑ or vLLM‑style serving stacks and speculative decoding systems such as ATLAS—grounded in a strong understanding of post‑training and inference theory, rather than purely theoretical algorithm design. You’ll work across the stack—from RL algorithms and training engines to kernels and serving systems—to build and improve frontier models via RL pipelines. People on this team are often spiky: some are more RL‑first, some are more systems‑first. Depth in one of these areas plus appetite to collaborate across (and grow toward more full‑stack ownership over time) is ideal. Requirements We don’t expect anyone to check every box below. People on this team typically have deep expertise in one or more areas and enough breadth (or interest) to work effectively across the stack. The closer you are to full‑stack (inference + post‑training/RL + systems), the stronger the fit—but being spiky in one area and eager to grow is absolutely okay. You might be a good fit if you: Have strong expertise in at least one of the following, and are excited to collaborate across (and grow into) the others: Systems‑first profile: Large‑scale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving. RL‑first profile: RL / post‑training for LLMs or large models (e.g., GRPO, RLHF/RLAIF, DPO‑like methods, reward modeling), and using these to train or fine‑tune real models. Model architecture design for Transformers or other large neural nets. Distributed systems / high‑performance computing for ML. Are comfortable working from algorithms to engines: Strong coding ability in Python Experience profiling and optimizing performance across GPU, networking, and memory layers. Able to take a new sampling method, scheduler, or RL update and turn it into a production‑grade implementation in the engine and/or training stack. Have a solid research foundation in your area(s) of depth: Track record of impactful work in ML systems, RL, or large‑scale model training (papers, open‑source projects, or production systems). Can read new RL / post‑training papers, understand their implications on the stack, and design minimal, correct changes in the right layer (training engine vs. inference engine vs. data / API). Operate well as a full‑stack problem solver: You naturally ask: “Where in the stack is this really bottlenecked?” You enjoy collaborating with infra, research, and product teams, and you care about both scientific quality and user‑visible wins. Minimum qualifications 3+ years of experience working on ML systems, large‑scale model training, inference, or adjacent areas (or equivalent experience via research / open source). Advanced degree in Computer Science, EE, or a related field, or equivalent practical experience. Demonstrated experience owning complex technical projects end‑to‑end. If you’re excited about the role and strong in some of these areas, we encourage you to apply even if you don’t meet every single requirement. Responsibilities Advance inference efficiency end‑to‑end Design and prototype algorithms, architectures, and scheduling strategies for low‑latency, high‑throughput inference. Implement and maintain changes in high‑performance inference engines (e.g., SGLang‑ or vLLM‑style systems and Together’s inference stack), including kernel backends, speculative decoding (e.g., ATLAS), quantization, etc. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Unify inference with RL / post‑training Design and operate RL and post‑training pipelines (e.g., RLHF, RLAIF, GRPO, DPO‑style methods, reward modeling) where 90+% of the cost is inference, jointly optimizing algorithms and systems. Make RL and post‑training workloads more efficient with inference‑aware training loops—for example, async RL rollouts, speculative decoding, and other techniques that make large‑scale rollout collection and evaluation cheaper. Use these pipelines to train, evaluate, and iterate on frontier models on top of our inference stack. Co‑design algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, and quickly identify bottlenecks across the training engine, inference engine, data pipeline, and user‑facing layers. Run ablations and scale‑up experiments to understand trade‑offs between model quality, latency, throughput, and cost, and feed these insights back into model, RL, and system design. Own critical systems at production scale Profile, debug, and optimize inference and post‑training services under real production workloads. Drive roadmap items that require real engine modification—changing kernels, memory layouts, scheduling logic, and APIs as needed. Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Provide technical leadership (Staff level) Set technical direction for cross‑team efforts at the intersection of inference, RL, and post‑training. Mentor other engineers and researchers on full‑stack ML systems work and performance engineering. About Together AI Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $280,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy    

false

Shape the Future of AI At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially. About Labelbox We're the only company offering three integrated solutions for frontier AI development: Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling Why Join Us High-Impact Environment: We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions. Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence. Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution. Continuous Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI. Clear Ownership: You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics. Role Overview As an Applied Research Engineer at Labelbox, you will be at the forefront of developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. Your role will involve designing and implementing advanced systems that align human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), etc. You will also work on innovative techniques to measure and improve human data quality, and develop AI-assisted tools to enhance the data labeling process. Your expertise in machine learning, frontier model training, and advanced human data alignment techniques will be crucial in pushing the boundaries of AI capabilities and delivering state-of-the-art solutions to meet the evolving needs of our customers. Your Impact Advance the field of AI alignment by developing cutting-edge methods, such as RLHF and novel approaches, that ensure AI systems reflect human preferences more accurately. Improve the quality of human-in-the-loop data by designing and deploying rigorous measurement and enhancement systems, leading to more reliable AI training. Increase efficiency and effectiveness in AI-assisted data labeling by creating tools that leverage active learning and adaptive sampling, reducing manual effort while improving accuracy. Shape the next generation of AI models by investigating how different types of human feedback (e.g., demonstrations, preferences, critiques) impact model performance and alignment. Optimize human feedback collection by developing novel algorithms that enhance how AI learns from human input, improving model adaptability and responsiveness. Bridge research and real-world application by integrating breakthroughs into Labelbox’s product suite, making human-AI alignment techniques scalable and impactful for users. Drive industry innovation by engaging with customers and the broader AI community to understand evolving data needs and share best practices for training frontier models. Contribute to the AI research ecosystem by publishing in top-tier journals, presenting at leading conferences, and influencing the future of human-centric AI. Stay ahead of AI advancements by continuously exploring new frontiers in human-AI collaboration, human data quality, and AI alignment, keeping Labelbox at the cutting edge. Establish Labelbox as a thought leader in AI by creating technical documentation, blog posts, and educational content that shape the industry's approach to human-centric AI development. What You Bring A strong foundation in AI and machine learning, backed by a Ph.D. or Master’s degree in Computer Science, Machine Learning, AI, or a related field. Proven experience (3+ years) in solving complex ML challenges and delivering impactful solutions that improve real-world AI applications. Expertise in designing and implementing data quality measurement and refinement systems that directly enhance model performance and reliability. A deep understanding of frontier AI models—such as large language models and multimodal models—and the human data strategies needed to optimize them. Proficiency in Python and experience with deep learning frameworks like PyTorch, JAX, or TensorFlow to prototype and develop cutting-edge solutions. A track record of publishing in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL) and contributing to the broader research community. The ability to bridge research and application by interpreting new findings and rapidly translating them into functional prototypes. Strong analytical and problem-solving skills that enable you to tackle ambiguous AI challenges with structured, data-driven approaches. Exceptional communication and collaboration skills, allowing you to work effectively across multidisciplinary teams and with external stakeholders. Labelbox Applied Research At Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios. We foster an environment of intellectual curiosity, collaboration, and innovation. We encourage our researchers to explore new ideas, engage in open discussions, and contribute to the wider AI community through publications and conference presentations. Our goal is to be at the forefront of human-centric AI development, setting new standards for how AI systems learn from and interact with humans.Labelbox strives to ensure pay parity across the organization and discuss compensation transparently.  The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.Annual base salary range$250,000—$300,000 USDLife at Labelbox Location: Join our dedicated tech hubs in San Francisco or Wrocław, Poland Work Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making Growth: Career advancement opportunities directly tied to your impact Vision: Be part of building the foundation for humanity's most transformative technology Our Vision We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs. Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs. Your Personal Data Privacy: Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice. Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.

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About the Company:Netomi is the leading agentic AI platform for enterprise customer experience. We work with the largest global brands like Delta Airlines, MetLife, MGM, United, and others to enable agentic automation at scale across the entire customer journey. Our no-code platform delivers the fastest time to market, lowest total cost of ownership, and simple, scalable management of AI agents for any CX use case. Backed by WndrCo, Y Combinator, and Index Ventures, we help enterprises drive efficiency, lower costs, and deliver higher quality customer experiences. Want to be part of the AI revolution and transform how the world’s largest global brands do business? Join us! About the Role Netomi is looking for a Solution Engineer - a key technical leader at the intersection of pre-sales engineering, AI architecture, and product innovation. This individual will design and implement agentic workflows that leverage the Netomi platform to power real-world enterprise solutions. You’ll work directly with enterprise clients and internal stakeholders to translate visionary AI concepts into practical, scalable systems - enabling AI agents to engage, reason, and act autonomously within complex customer ecosystems. Responsibilities Partner with Account Executives to discover and scope customer challenges, designing high-value technical solutions that showcase the ROI of Netomi’s platform. Architect and build agentic workflows that integrate generative AI with APIs, databases, and enterprise tools to power experiences for our customer’s end users. Develop custom demonstrations, prototypes, and proofs of concept using the Netomi platform tailored to specific clients use cases. Design, test, and refine prompts and AI orchestration chains to optimize performance, reasoning, and reliability across varied use cases. Communicate complex technical concepts clearly and persuasively to audiences ranging from C-level executives to hands-on engineers. Collaborate with product and engineering teams, contributing insights from customer engagements to inform roadmap priorities. Document and present solution designs, workflows, and technical configurations for both internal and client-facing reference. Requirements 1-2 years of experience in a customer-facing sales engineering or solutions engineering role, ideally in AI, automation, or enterprise SaaS. Hands-on experience with AI prompt design, workflow orchestration, and integrating REST APIs, webhooks, and cloud services (AWS, GCP, or Azure). Working knowledge of JavaScript, Python, or related scripting languages for building integrations and automation logic. Proven ability to architect and communicate end-to-end technical solutions that align AI capabilities with business outcomes. Functional understanding enterprise software ecosystems, and data flow patterns. Excellent communication, presentation, and interpersonal skills with a record of collaboration across technical and non-technical teams. Preferred Qualifications Experience in agentic or autonomous AI systems (e.g., LangChain, LlamaIndex, or similar frameworks). Familiarity with MLOps, AI governance, and compliance in production-scale deployments. Background working in high-growth startup environments. Awareness of UX/UI principles for designing customer-facing AI experiences. Netomi is an equal opportunity employer committed to diversity in the workplace. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, disability, veteran status, and other protected characteristics.

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  About the Company Armada is a full-stack edge infrastructure company delivering compute, connectivity, and sovereign AI/ML to some of the world’s most remote places. Named one of Fast Company's Most Innovative Companies, Armada’s solutions are deployed in over 60 countries globally for organizations ranging from energy to defense.    With over $200 million in funding, Armada is backed by top investors such as Microsoft (M12), Founders Fund, and has strategic partnerships including Starlink, Skydio, and NVIDIA. We are looking for the most brilliant minds in the world to join us.    Working at Armada means taking ownership, driving autonomy, and delivering impact. You’ll tackle challenges that haven’t been solved before and help build something transformative from the ground up. What you do here will not only define your career but help further Armada’s mission to bridge the digital divide for customers around the world.      About the role At Armada, we are unlocking the limitless potential of AI to transform operations and improve lives in some of the most remote locations on Earth. From the expansive mines of Australia to the oil fields of Northern Canada, and the coffee plantations of Colombia, Armada offers a unique opportunity to tackle exciting AI and ML challenges on a global scale. We are actively seeking passionate AI Engineers with hands-on expertise across a range of domains, including real-time computer vision, statistical machine learning, natural language processing, transformers, control and navigation, reinforcement learning, and large-scale distributed AI systems. Ideal candidates will possess strong skills in machine learning (ML), deep learning (DL), and real-time computer vision techniques. You will be responsible for building ML/DL models tailored to specific challenges, preparing datasets for testing, evaluating model performance, and deploying solutions in production environments. Familiarity with containerization, microservices architecture, and the ability to independently deploy ML models into production is essential. If you are a self-driven individual with a passion for cutting-edge AI, we want to hear from you. Armada offers an unparalleled opportunity to confront some of the most thrilling AI and ML challenges in the world. Join our dynamic AI Engineering team as we deliver disruptive edge-compute systems capable of autonomous learning, prediction, and adaptation using vast, real-time datasets. We are pioneers in developing high-performance computing solutions for self-driving cars, camera networks, robotics, drones, conversational agents, and real-time monitoring and diagnostic systems. Our vision is to empower AI systems to seamlessly and securely interact with the complexities and uncertainties of the real world, and our mission is to bridge the digital divide in the process.  Location. This role is office-based at our Bellevue, Washington office.  What You'll Do (Key Responsibilities) Translating business requirements into requirements for AI/ML models. Preparing data to train and evaluate AI/ML/DL models. Building AI/ML/DL models by applying state-of-the-art algorithms, especially transformers. In some cases, leverage existing algorithms from academic or industrial research. Testing, evaluating the AI/ML/DL models, benchmarking their quality, and publishing the models, data sets, and evaluations. Deploying the models in production by containerizing the models. Working with customers and internal employees to refine the quality of the models. Establishing continuous learning pipelines for models with online learning or transfer learning. Building and deploying containerized applications on the cloud or on-premise environments Required Qualifications BS or MS degree in computer science, computational. science/engineering, or related technical field (or equivalent experience). 3+ years of work-related experience in software development with good Python, Java, and/or C/C++ programming skills. Familiarity with containers, numeric libraries, modular software design. Hands-on expertise with traditional statistical machine learning techniques as well as deep-learning and natural language processing modeling. Expertise in supervised, unsupervised, and transfer learning techniques. Hands-on expertise in machine learning techniques and algorithms with a strong background in state-of-the-art DNN architectures (Transformers, CNN, R-CNN, RNN, BERT, GAN, autoencoders, etc.) and experience in developing or using major deep learning frameworks (e.g., PyTorch, Tensorflow, etc). Experience with solving and using machine learning for real-world problems. Preferred Experience and Skills Demonstrable experience in building, programming, and integrating software and hardware for autonomous or robotic systems. Proven experience producing computationally efficient software to meet real-time requirements. Background with container platforms such as Kubernetes. Strong analytical skills with a bias for action. Strong time-management and organization skills to thrive in a fast-paced, dynamic environment. Solid written and oral communications skills. Good teamwork and interpersonal skills. Compensation For U.S. Based candidates: To ensure fairness and transparency, the starting base salary range for this role for candidates in the U.S. are listed below, varying based on location experience, skills, and qualifications. In addition to base salary, this role will also be offered equity and subsidized benefits (details available upon request). Benefits Competitive base salary and equity Medical, dental, and vision (subsidized cost) Health savings accounts (HSA), flexible spending accounts (FSA), and dependent care FSAs (DCFSA) Retirement plan options, including 401(k) and Roth 401(k) Unlimited paid time off (PTO) 14 paid company holidays per year #LI-SM2 #LI-Onsite   Compensation$154,560—$193,200 USD  You're a Great Fit if You're A go-getter with a growth mindset. You're intellectually curious, have strong business acumen, and actively seek opportunities to build relevant skills and knowledge  A detail-oriented problem-solver. You can independently gather information, solve problems efficiently, and deliver results with a "get-it-done" attitude  Thrive in a fast-paced environment. You're energized by an entrepreneurial spirit, capable of working quickly, and excited to contribute to a growing company A collaborative team player. You focus on business success and are motivated by team accomplishment vs personal agenda  Highly organized and results-driven. Strong prioritization skills and a dedicated work ethic are essential for you    Equal Opportunity Statement At Armada, we are committed to fostering a work environment where everyone is given equal opportunities to thrive. As an equal opportunity employer, we strictly prohibit discrimination or harassment based on race, color, gender, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other characteristic protected by law. This policy applies to all employment decisions, including hiring, promotions, and compensation. Our hiring is guided by qualifications, merit, and the business needs at the time.   Unsolicited Resumes and Candidates Armada does not accept unsolicited resumes or candidate submissions from external agencies or recruiters. All candidates must apply directly through our careers page. Any resumes submitted by agencies without a prior signed agreement will be considered unsolicited and Armada will not be obligated to pay any fees.  

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Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities.Tenstorrent is seeking an Physical Design Engineer to lead  cross-functional efforts to solve complex physical design challenges and develop end-to-end RTL-to-GDS methodologies across advanced nodes, with a strong focus on PPA and runtime improvements. The engineer will architect, integrate, and deploy AI/ML-driven solutions into production physical design flows, creating custom CAD tools and partnering with internal teams and EDA vendors to drive next-generation, ML-enabled capabilities.  This role is hybrid, based out of Santa Clara, CA or Austin, TX or Fort Collins, CO. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.   Who you are BS in Electrical or Computer Engineering (or equivalent experience) with 5+ years in Physical Design CAD methodology at advanced nodes. Proven track record improving PPA and/or runtime on high-performance, low-power taped-out designs. Hands-on with industry-standard EDA tools (e.g., Fusion Compiler) across synthesis, P&R, STA, signoff, and hierarchical flows. Strong Python/Tcl and data skills, with interest or experience in ML frameworks (PyTorch, TensorFlow), and the ability to drive complex projects independently.   What we need Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, P&R, STA, signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and QoR. Optimize EDA tools and custom CAD flows using data-driven and ML-based techniques, in close collaboration with verification, extraction, timing, DFT, and EDA vendors.   What you will learn How to scale AI/ML-driven methodologies across diverse products and advanced technology nodes in real production flows. New ways to blend classical EDA algorithms with modern ML techniques to push PPA and runtime limits. Best practices for deploying, validating, and monitoring ML models in production CAD environments. How to influence next-generation ML-enabled EDA tools and collaborate deeply with cross-functional teams (PV, extraction, timing, DFT).   Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made. Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer. This position requires access to technology that requires a U.S. export license for persons whose most recent country of citizenship or permanent residence is a U.S. EAR Country Groups D:1, E1, or E2 country. This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology.  Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2).   These requirements apply to persons located in the U.S. and all countries outside the U.S.  As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency.  If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.

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Who We Are PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials.  Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes. Where You Fit  As the Associate Director, MLOps Lead, you will lead the team responsible for the backbone of our AI/ML Stack: the infrastructure that bridges ML research and massive-scale production. Your primary directive is to evolve our stack to meet the next scale of needs in large scale ML training & inference workloads.   You’re someone who enjoys designing and building for reliability, relishes collaboration and technical challenges, and takes pride in making things better – without taking yourself too seriously. Our technical space is broad: high-scale AI training & inference workloads, cloud infrastructure, Kubernetes, observability, distributed systems, and a bit of everything in between. What You’ll Do This role is critical for driving the scalability and efficiency of our Machine Learning Operations platform with high-impact & high growth strategic initiatives.  Vision and Roadmap: Develop and execute the long term vision & roadmap for MLOPs team to support ML development and deployment needs across the business units. Successfully manage the tension between short-term tactical deliveries and long-term architectural transformation for future growth.  Team Management: Lead and mentor a team of 6-7+ high-performing engineers. Strategically allocate resources to manage support for existing services while executing key strategic initiatives. Cross-Functional Collaboration: Partner with leaders across machine learning, data science, product engineering, and infrastructure to proactively identify pain points, address bottlenecks, and facilitate the deployment of new solutions. Foundation Model Readiness: Architect the compute and storage pipelines required for ML Engineers to manage millions of slides and complex derived artifacts without data fragmentation or synchronization latency. Inference Modernization: Modernize the AI Product inference stack to support 5-10x growth of AI runs across global deployments. System Observability: Collaborate with Site Reliability Engineering (SRE) to establish comprehensive metrics covering compute under-utilization, network bottlenecks, and granular cost and turn-around-time attribution. Technology Refresh: Conduct "Build vs. Buy" assessments, leading "Stack Refresh" audits to benchmark our proprietary tools against best-in-class commercial and open-source alternatives to meet our future needs. What You Bring To be successful in this role with us, you'll at least need: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience). 2-3+ years of experience managing engineering team(s), with a focus on building production-grade frameworks for MLOps or ML Infrastructure. Deep technical expertise with ML workloads on kubernetes, cloud computing platforms (AWS/GCP/Azure), workflow orchestration (Airflow, Kubeflow, or proprietary equivalents) and DevOps principles and infrastructure-as-code (Helm, Terraform). Proven experience managing petabyte-scale datasets and high-throughput production inference pipelines. Strong software engineering skills in complex, multi-language systems and experience with scalable service architecture. Use of AI assistants (e.g. CoPilot, Cursor, Claude) across platform development lifecycle. It Would Be Great If You Also Have Exposure to ML frameworks like PyTorch or Scikit-learn. Experience with large-scale data processing frameworks (e.g. Spark, Hive, Databricks, Amazon EMR) Expertise in MLOps principles, including model lifecycle management, feature stores, model monitoring, and CI/CD for ML. Familiarity with security and compliance best practices in ML systems. We Want To Hear From You At PathAI, we are looking for individuals who are team players, are willing to do the work no matter how big or small it may be, and who are passionate about everything they do. If this sounds like you, even if you may not match the job description to a tee, we encourage you to apply. You could be exactly what we're looking for.  PathAI is an equal opportunity employer, dedicated to creating a workplace that is free of harassment and discrimination. We base our employment decisions on business needs, job requirements, and qualifications — that's all. We do not discriminate based on race, gender, religion, health, personal beliefs, age, family or parental status, or any other status. We don't tolerate any kind of discrimination or bias, and we are looking for teammates who feel the same way. The cash compensation outlined below includes base salary or hourly wage and on-target commission for employees in eligible roles. The summary below indicates if an employee in this position is eligible for annual bonus, overtime pay and equity awards. Individual compensation packages are tailored based on skills, experience, qualifications, and other job-related factors.  Annual Pay Range: AD, MLOps: $181,500 - $278,300 Not Overtime Eligible Eligible for Equity

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About Mistral -At Mistral AI, we are a tight-knit, nimble team dedicated to bringing our cutting-edge AI technology to the world. Our mission is to make AI ubiquitous and open.  -We are creative, low-ego, team-spirited, and have been passionate about AI for years. -We hire people who thrive in competitive environments, because they find them more fun to work in. -We hire passionate women and men from all over the world. -Our teams are distributed between France, UK and USA    Role Summary  -Mistral AI is hiring an AI Developer Relations Engineer to join our team and actively contribute to the community, lead developer relations initiatives, and engage in development and integration of open-source AI ecosystem projects. -The role reports to our Head of Developer Relations  -The role is located in Singapore    Key Responsibilities  -Develop high-quality documentation, tutorials, and sample code that enable developers to effectively understand and utilize Mistral AI's products and integrations. -Contribute to the community by resolving issues, providing answers, and offering guidance to users, fostering a supportive and collaborative environment. -Lead developer relations initiatives and develop programs to engage the community. -Organize and participate in community events centered around Mistral AI solutions. -Work closely with the AI community to develop and maintain integrations, ensuring seamless compatibility and optimal performance. -Remain informed about industry trends, best practices, and emerging technologies in AI, continuously updating your knowledge and skills.   Qualifications & profile  -Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience. -Passion for AI, with a constant drive to stay updated and ahead of the curve. -Fluency in Python Familiarity with the AI ecosystem and popular technology stacks including AI orchestration tools, vector databases, and agent frameworks  -Familiar with the Pytorch and transformers architectures -Proven experience working with AI or machine learning projects, demonstrating a solid understanding of the AI landscape. -Strong communication skills with an ability to explain complex technical concepts in simple terms -Demonstrated ability to manage projects and lead them to successful completion. -Ability to work collaboratively in a team environment.

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About Snorkel At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!About the Role Snorkel AI is hiring Frontier AI Solutions Engineers who will partner with leading AI labs on their most challenging data problems. This is a high-impact, customer-facing role that combines technical depth with strong presales instincts. You'll partner with customer research teams to design complex data and environments that improve frontier model performance, demonstrating Snorkel's capabilities through research-driven engagements. You'll work at the critical intersection of research, technical strategy, and customer partnership. This includes scoping training data needs, designing RL environments and tasks, developing evaluation frameworks, probing model behavior and failure modes, and translating customer research objectives into actionable technical plans. You'll develop technical specifications, analyze frontier model failure modes, and serve as a thought partner to customer research teams throughout the sales cycle and into early delivery phases. Main Responsibilities Partner with frontier AI research labs to design datasets and environments that improve model performance Lead technical conversations with customer researchers to understand model capabilities, failure modes, data requirements, and success criteria Probe model behavior through systematic evaluation to uncover weaknesses and identify high-impact data interventions Design evaluation frameworks, calibration processes, and quality rubrics that establish measurable project success metrics Develop technical specifications for data projects that balance research rigor with operational feasibility Serve as thought partner to customer research teams throughout the sales cycle, building trust and credibility Stay current on frontier AI research, RL environment design, post-training techniques, and evaluation methodologies Preferred Qualifications Strong expertise in frontier AI concepts including LLMs, training data pipelines, evaluation methodologies, post-training techniques (RLHF, DPO, RLAIF), and domain areas such as coding agents, reasoning, multimodal models, or RL environments Experience in applied ML research, data science, or research-intensive technical roles with customer-facing or collaborative research experience Proficiency in Python and familiarity with ML frameworks and LLM APIs Excellent communication skills — ability to deliver technical presentations and explain complex concepts to diverse audiences Familiarity with data curation workflows, synthetic data generation, LLM-as-a-Judge, or evaluation framework design Ability to work in a fast-moving environment, comfortable with ambiguity and rapid iteration B.S. in Computer Science, Machine Learning, or related field with 4+ years of experience in AI/ML solutions engineering or technical customer-facing roles Compensation range for Tier 1 locations of San Francisco Bay Area and New York City, $172K - $300K OTE. All offers also include equity in the form of employee stock options. Our compensation ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Why Join Snorkel AI? At Snorkel AI, we're building the future of data-centric AI. Our Expert Data-as-a-Service organization partners with world-class customers to solve some of the hardest data challenges — creating training and evaluation data that power the next generation of LLMs and AI systems. You'll work directly on projects that impact real production systems, while shaping how internal teams deliver faster, better, and more intelligently. This is a rare opportunity to own technical data workflows and be a founding member of the technical DaaS team.  #LI-CG1 Salary Range  -   Salary Range $172,000—$300,000 USDBe Your Best at Snorkel Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success. Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, performance, merit, and business need. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

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About KreaAt Krea, we are building next-generation AI creative tools.We are dedicated to making AI intuitive and controllable for creatives. Our mission is to build tools that empower human creativity, not replace it.We believe AI is a new medium that allows us to express ourselves through various formats—text, images, video, sound, and even 3D. We're building better, smarter, and more controllable tools to harness this medium.Supercomputing / AI Infra at KreaWe build and operate the infrastructure for Krea's research and inference. Distributed training, 1000+ K8s GPU clusters, petabyte scale data pipelines, etc. We build a lot of this from scratch — custom distributed datastores, job orchestration systems, and streaming pipelines that replace tools like Kafka and Ray for modern AI workloads at scale.Example projects:Distributed data systemsDesign multi-stage pipelines that turn petabytes of raw data into clean, annotated datasetsRun classification models on billions of imagesDeploy and combine LLMs to caption massive multimedia dataGPU infrastructureManage distributed training and inference on 1000+ GPU Kubernetes clustersSolve orchestration and scaling for large-scale GPU job processingScale workloads and research between clusters in multiple datacentersDistributed trainingProfile and optimize dataloaders streaming thousands of images per secondProfile and debug InfiniBand networking on huge training runsBuild fault tolerance systems for large-scale pretrainingCollaborate with researchers on evolving RL infrastructureApplied ML pipelinesFind clean scenes in millions of videos using distributed shot-boundary detectionCustomize and train models to filter billions of images for questions like "is this a screenshot?"Build the systems that bridge raw cluster capacity and research outputWho we're looking for:Systems people. If you've read a blog post about InfiniBand debugging or building a custom distributed database and thought "I want to do that" — this is that team.You'll spend your time working heavily with Python, Kubernetes, Torch, and data tools like DuckDB, Arrow, etc. It's OK if you don't have K8s or ML experience — the main thing we hire for is an intuition for distributed systems, and a great mental model of how systems interact and function under different conditions.Strong candidates may have experience with…Python, PyArrow, DuckDB, SQL, massive relational databases, PyTorch, Pandas, NumPy…KubernetesDesigning and implementing large-scale ETL systemsFundamental knowledge of containerization, operating systems, file-systems, and networkingDistributed systems designDistributed training systems (NCCL, InfiniBand, RDMA)Streaming and event processing systems (Kafka, Pulsar, or similar)PyTorch internals, custom dataloaders, and training infrastructure