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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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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 ElevenLabsElevenLabs is an AI research and product company transforming how we interact with technology.We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms:ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale.ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages.ElevenAPI gives developers access to our leading AI audio foundational models.Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you.How we workHigh-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy.Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you.AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations.Excellence everywhere: Everything we do should match the quality of our AI models.Global team: We prioritize your talent, not your location.What we offerInnovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible.Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities.Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend.Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose.Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy.Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend.About the roleWe're hiring our first Sales Enablement Systems Lead to reinvent how ElevenLabs manages, organizes, and delivers internal knowledge to our Sales teams. This is a 0-to-1 builder role. You will architect the systems, build the infrastructure, and create the AI-powered tools that make our Revenue team smarter and faster.What sets this role apart: we're not looking for someone to manage legacy systems — we're looking for someone to build something new. You've looked at traditional LMS, CMS, and knowledge management tools and thought "I could build something better." You understand that the future of enablement is AI-native: content that auto-updates, surfaces in workflow when people need it, and learns from how teams actually use it. You're part strategist, part architect, part developer.This role sits within Sales Enablement and reports to the Sales Enablement Lead, International. You will work in close partnership with:RevOps — building GTM agent swarms and AI infrastructureProduct Marketing — managing sales and marketing contentCompany Ops — coordinating cross-functional knowledge managementSales Enablement — surfacing content to sellers at the right timeThis is a player-coach role. There are no direct reports initially; as the function scales, there may be an opportunity to build a team.What you'll doOwn content and knowledge architectureAudit and map the current content landscape across Sales Hub, WorkRamp, Fern wiki, Google Drive, and other systemsDesign a unified content architecture that brings order to how sales content is organized, tagged, and maintainedCreate governance frameworks for how content is created, updated, and retired across teamsBuild the single source of truth that sales teams have been asking forBuild the enablement infrastructureOwn and evolve our LMS (WorkRamp) and internal knowledge systemsEvaluate, select, and implement tools that modernize how we manage and deliver contentBuild integrations that connect content systems to seller workflows (Salesforce, Slack, Gong, etc.)Design personalized learning paths that adapt to role, tenure, and skill gapsCreate AI-powered enablement toolsBuild and deploy sales agents that surface the right content at the right timeContribute to the GTM agent swarm strategy alongside RevOpsDesign agents that answer seller questions, prep for meetings, and automate repetitive enablement tasksUse ElevenLabs' own voice and agent technology to power enablement experiencesDrive agentic enablementDefine what "Agentic Enablement" looks like at ElevenLabsBuild AI-powered coaching tools, role-play agents, and just-in-time learning experiencesCreate systems where content auto-updates based on product releases, competitive intel, and market changesCoordinate cross-functional knowledge managementPartner with Product Marketing to ensure sales content flows into the right systemsPartner with Company Ops to align sales knowledge management with company-wide standardsPartner with RevOps to ensure agent infrastructure is built on solid content foundationsMeasure and improveDefine and track system health metrics: content freshness, usage, findability, and time-to-answerBuild dashboards that show whether enablement systems are actually helping sellersUse data to continuously improve content architecture and agent performanceShape the functionDocument best practices, establish operating rhythms, and influence how the systems function evolves as Sales Enablement scalesWhat success looks like (first 6–9 months)One centralized content hub live and adopted by sales teamsMeasurable reduction in "where do I find X?" questionsFirst wave of sales agents deployed (meeting prep, content search, onboarding assistant)Content architecture documented and governance model in placeClear roadmap for agentic enablement buildoutCross-functional alignment on how content flows between Product Marketing, Ops, and EnablementRequirements4–7+ years of experience in enablement operations, knowledge management, sales ops, RevOps, or a similar systems-focused roleHands-on building experience: you've built systems, automations, or tools — not just managed themTechnical fluency: comfortable with APIs, integrations, and modern AI tools; you can read code, debug issues, and build in low-code/no-code environmentsClaude Code proficiency (or equivalent): you can build with AI coding tools and verify their output without relying solely on natural languageExperience with LMS, CMS, or knowledge management platforms (WorkRamp, Highspot, Seismic, Guru, Notion, Confluence, etc.)Experience building or working with AI agents, chatbots, or conversational AI toolsStrong systems thinking: you can map complex processes, identify dependencies, and design elegant solutionsExperience working cross-functionally with Product Marketing, Ops, and Sales teamsExcellent communication skills: you can translate technical concepts for non-technical audiencesBias for action: you ship fast, iterate, and don't wait for permissionAdditional:Experience at a high-growth AI/ML, developer tools, or API-first companyBackground in martech, content personalization, or audience segmentationExperience with Retool, Airtable, or similar tools for building internal applicationsSQL or Python proficiency for data analysis and automationExperience building GTM agents or AI-powered sales toolsFamiliarity with ElevenLabs' products (Agents Platform, API, Creative Platform)LocationRemote-first, globally distributed role. We have a strong preference for candidates based in Europe, the UK, or the US East Coast, to enable close collaboration with the Sales Enablement Lead, International, RevOps, Product Marketing, and London HQ. Exceptional candidates outside these regions will be considered if they can operate effectively across European and US time zones.Why this role mattersContent chaos is one of the biggest pain points in sales today. Sellers can't find what they need. Content gets stale. Knowledge lives in ten different places. Traditional tools weren't built for this.The systems you build will define how ElevenLabs scales its Revenue team's knowledge layer — directly impacting seller productivity, ramp time, and revenue. If you're excited to build from zero, work at the intersection of enablement and AI, and shape a foundational function at one of the fastest-growing AI companies in the world, we'd love to hear from you.

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Remote role, only for applicants in Argentina and Uruguay. The Salesforce Technical Architect is responsible for designing and delivering scalable, enterprise-grade cloud solutions across the Salesforce ecosystem. This role focuses on architecting AI-enabled, data-driven, and integrated enterprise platforms leveraging Salesforce Agentforce, Data Cloud, MuleSoft, and core Salesforce Clouds. The architect will design end-to-end solutions spanning CRM, AI agents, unified data platforms, and enterprise integrations, while ensuring adherence to Salesforce best practices, platform guardrails, and enterprise architecture standards. The role requires deep expertise in Salesforce development, AI-driven automation, integration architecture, and modern iPaaS platforms. This position works closely with delivery teams, client stakeholders, and cross-platform architects to deliver scalable, secure, and intelligent enterprise solutions. Responsibilities: Architecture & Solution Design Architect end-to-end Salesforce solutions across Sales Cloud, Service Cloud, Experience Cloud, Data Cloud, and Agentforce AI agents. Design AI-enabled workflows and autonomous agents using Agentforce to improve customer engagement, automation, and operational efficiency. Architect enterprise data strategies leveraging Salesforce Data Cloud, including identity resolution, unified customer profiles, and real-time data activation. Define scalable enterprise integration architectures using MuleSoft or other iPaaS platforms, APIs, and event-driven patterns. Integration & Data Strategy Design API-led connectivity frameworks using MuleSoft or comparable iPaaS platforms. Architect integrations between Salesforce and enterprise systems such as ERP, data warehouses, marketing platforms, and external SaaS applications. Define data ingestion, transformation, and activation strategies across Salesforce Data Cloud and external data platforms. Platform Development & Technical Oversight Provide technical leadership for development teams across Apex, Lightning Web Components (LWC), Visualforce, and Salesforce automation frameworks. Contribute hands-on when needed to validate architecture, unblock teams, or implement complex components. Ensure solutions are designed for high performance, scalability, security, and large data volumes. Delivery Leadership Partner with Delivery Managers, consultants, and client stakeholders to translate business requirements into scalable technical architectures. Lead design reviews, code reviews, and architectural governance across Salesforce implementations. Mentor developers and consultants on platform best practices, architecture patterns, and integration design. AI & Innovation Evaluate and implement Salesforce AI capabilities including Agentforce and generative AI-powered automation. Identify opportunities to leverage AI-driven agents, predictive insights, and real-time data activation. Drive innovation initiatives to continuously improve the firm’s Salesforce architecture and delivery capabilities. Client Advisory Serve as a trusted technical advisor to clients, helping them understand architectural trade-offs, platform capabilities, and long-term scalability considerations. Guide clients on AI adoption, unified data strategies, and enterprise integration modernization. Practice Development Contribute to internal architecture standards, accelerators, and reusable frameworks. Lead knowledge sharing around Agentforce, Data Cloud, and integration architecture. Support presales and solutioning efforts for complex Salesforce engagements. Qualifications & Skills: 8+ years of experience designing and implementing Salesforce solutions. 5+ years of experience architecting enterprise systems and leading client-facing implementations. Experience delivering large-scale Salesforce implementations for mid-market or enterprise organizations. Strong development experience in Apex, SOQL/SOSL, Lightning Web Components (LWC), and Salesforce automation tools (Flow, Platform Events). Deep understanding of Salesforce platform architecture, governor limits, security models, and performance optimization. Experience designing solutions using Salesforce Data Cloud. Experience with Salesforce Agentforce or AI-powered automation frameworks. Strong experience designing integrations using REST, SOAP, GraphQL APIs, and event-driven architectures. Hands-on experience with MuleSoft or similar iPaaS platforms such as Celigo, Boomi, Workato or Informatica Cloud. Knowledge of API-led connectivity, microservices architecture, and enterprise integration patterns. Strong knowledge of JavaScript, HTML, CSS, and modern front-end frameworks used within Salesforce ecosystems. Experience working with complementary platforms such as AWS, Heroku, GCP, Azure or other cloud infrastructure. Familiarity with data platforms, data lakes, and modern analytics architectures is a plus. Ability to communicate complex technical concepts to both technical and non-technical stakeholders. Strong problem-solving skills and ability to operate in fast-paced consulting environments. Self-starter mindset with a passion for learning new technologies and driving innovation. Salesforce Application Architect, System Architect, Salesforce Data 360 and Salesforce AI Certifications Preferred About RYZ Labs: RYZ Labs is a startup studio built in 2021 by two lifelong entrepreneurs. The founders of RYZ have worked at some of the world's largest tech companies and some of the most iconic consumer brands. They have lived and worked in Argentina for many years and have decades of experience in Latam. What brought them together is the passion for the early phases of company creation and the idea of attracting the brightest talents in order to build industry-defining companies in a post-pandemic world.   Our teams are remote and distributed throughout the US and Latam. They use the latest cutting edge technologies in cloud computing to create applications that are scalable and resilient. We aim to provide diverse product solutions for different industries, planning to build a large number of startups in the upcoming years.   At RYZ, you will find yourself working with autonomy and efficiency, owning every step of your development. We provide an environment of opportunities, learning, growth, expansion and challenging projects. You will deepen your experience while sharing and learning from a team of great professionals and specialists.   Our values and what to expect: - Customer First Mentality - every decision we make should be made through the lens of the customer. - Bias for Action - urgency is critical, expect that the timeline to get something done is accelerated. - Ownership -  step up if you see an opportunity to help, even if not your core responsibility.  - Humility and Respect - be willing to learn, be vulnerable, and treat everyone that interacts with RYZ with respect. - Frugality - being frugal and cost conscious helps us do more with less. - Deliver Impact - get things done in the most efficient way.  - Raise our Standards - always be looking to improve our processes, our team, our expectations. Status quo is not good enough and never should be.

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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

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About Cantina:Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning.If you're excited about the potential AI has to shape human creativity and social interactions, join us in building the future!About the Role:We are looking for an Applied ML Engineer to build and scale the data pipelines behind our large video generation models. This role is focused on collecting large amounts of relevant video data, preparing high-quality training samples, and developing robust preprocessing, filtering, and parsing workflows. You'll orchestrate annotation pipelines across platforms such as MTurk and own the full lifecycle of training data, from raw ingestion to clean, model-ready samples that directly drive quality improvements. This role sits at the intersection of data engineering and ML research, making it central to how we turn messy real-world data into the fuel that moves our models forward.What You’ll Do:Build and maintain data pipelines for large video generation models, including data ingestion, parsing, filtering, preprocessing, and dataset curation at scale, using tools such as AWS S3 and DynamoDB.Design and run annotation workflows across platforms such as MTurk, Prolific, and Mechanical Turk, including task design, quality control, and label validation.Train, evaluate, and improve smaller supporting models used for data filtering, quality assessment, preprocessing, or other parts of the ML pipeline.Partner closely with research and engineering teams to turn experimental workflows into scalable, repeatable systems that support model training and evaluation.Own data quality across the pipeline by identifying bottlenecks, failure modes, and low-quality sources, and continuously improving tooling and processes.Build internal tools and automation that make it easier to prepare datasets, launch annotation jobs, monitor outputs, and support model development end to end.Drive larger pipeline projects from start to finish, such as new dataset creation efforts or upgrades to labeling and preprocessing infrastructure.Work within a Kubernetes-based training infrastructure, ensuring datasets are properly prepared, formatted, and delivered to training clusters.Profile and optimize research model inference scripts used in preprocessing steps, ensuring that model-driven filtering and transformation stages run within practical time and cost constraints when applied to large-scale raw data.What You’ll Bring:3+ years of experience in machine learning, applied ML, data pipelines, or related engineering roles, ideally working on large-scale multimodal, video, or vision-based systems.Strong programming skills in Python and solid experience building reliable data processing and preprocessing pipelines for ML workflows.Hands-on experience preparing training data for ML models, including parsing, filtering, dataset curation, quality control, and large-scale data handling using tools such as AWS S3 and DynamoDB.Familiarity with annotation and labeling workflows, including task design, vendor or crowd-platform orchestration such as MTurk or Prolific, and methods for ensuring label quality.Experience working with Kubernetes for orchestrating distributed workloads, including data preprocessing, pipeline execution, and dataset delivery to training clusters.Comfort working across cloud and on-demand compute environments such as AWS and RunPod, with the ability to port and optimize pipelines across infrastructure.Familiarity with distributed data processing frameworks and experience designing systems that operate reliably at scale across many nodes or workers.Working knowledge of PyTorch and the broader deep learning stack, with the ability to read, debug, and optimize research model inference code for use in production preprocessing pipelines.Ability to work cross-functionally with research and engineering teams and translate experimental ideas into robust, scalable systems.Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field; experience in generative video, computer vision, or multimodal ML is strongly preferred.Bonus: Experience training, evaluating, or fine-tuning smaller ML models used for classification, filtering, ranking, quality assessment, or other supporting tasks in an ML pipeline.Compensation:The anticipated annual base salary range for this role is between $200,000-$260,000 (€170,000-€225,000). When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.Benefits for U.S.-based roles: Competitive salary and generous company equityMedical, dental, and vision insurance – 99.99% of premiums covered by Cantina42 days of paid time off, including:15 PTO days10 sick days15 company holidays2 floating holidaysGenerous parental leave & fertility support401(k) retirement savings planLifestyle spending account – $500/month to use however you’d likeComplimentary lunch and snacks for in-office employeesOne Medical membership, and more!

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We are looking for the best42dot의 Senior Computer Vision Engineer는 안전한 자율주행 기술들을 연구 개발합니다. 고도화된 컴퓨터비전과 기계학습 기술을 활용하여 자율주행차에서 취득되는 다양한 시각정보들을 처리하고, 사람 수준의 자율주행 인식 기능을 구현합니다.As a Senior Computer Vision Engineer at 42dot, you will focus on researching and developing advanced autonomous driving technologies. Utilizing sophisticated computer vision and machine learning techniques, you will process various visual data acquired from autonomous vehicles and implement human-level autonomous driving perception capabilities.Responsibilities 자율주행 기술구현을 위한 3차원 컴퓨터비젼 및 기계학습 알고리즘 연구 개발3D shape modeling and processingObject pose estimation and trackingEfficient and scalable visionVision and roboticsLow-level and physics-based visionSelf-supervised representation learning from large-scale unlabeled scene dataWorld models and closed-loop simulation for autonomous drivingConduct research and develop 3D computer vision and machine learning algorithms for integrating autonomous driving technologyPerform 3D shape modeling and processing tasksImplement object pose estimation and tracking algorithmsDevelop efficient and scalable vision solutionsExplore the intersection of vision and roboticsWork on low-level and physics-based vision algorithmsSelf-supervised representation learning from large-scale unlabeled scene dataWorld models and closed-loop simulation for autonomous drivingQualifications 유관 경력 7년차 이상컴퓨터비젼, 로보틱스, 또는 기계학습과 관련된 전공의 석사/박사 학위 이상 혹은 동등한 경력컴퓨터비젼 및 기계학습에 대한 이론 및 실무 지식뛰어난 프로그래밍 기술(C/C++, Python 등)Master’s (MS) or Ph.D. in Computer Vision, Robotics, Machine Learning, or a related field7+ years of relevant experience or equivalent practical experienceStrong theoretical and practical knowledge of computer vision and machine learning algorithmsProficiency in programming languages such as Python and C/C++Preferred Qualifications 자율주행 및 로보틱스 관련 연구 개발 경험GPS, IMU, 카메라, LIDAR 와 같은 다양한 센서를 사용한 개발 경험VR/AR 관련 어플리케이션 개발 경험병렬 프로그래밍 및 시스템 최적화 관련 개발 경험관련 분야 저서/학술활동 이력(CVPR, ICCV, ECCV, IJCV, TIP, TPAMI 등)Proven experience in projects or commercial systems related to autonomous driving, robotics, or 3D visionHands-on experience with multi-sensor data processing and fusion (GPS, IMU, cameras, LiDAR)Expertise in parallel programming (e.g., CUDA, OpenCL) and system optimizationDemonstrated experience in SLAM, depth estimation, 3D reconstruction, or pose estimation algorithm designExperience in developing VR/AR applicationsStrong academic background with publications in conferences/journals such as CVPR, ICCV, ECCV, TPAMI, or IJCVExperience deploying and managing ML models in large-scale cloud environmentsInterview Process 서류전형 - 코딩테스트 - 화상면접 (1시간 내외) - 대면 혹은 화상면접 (3시간 내외) - 최종합격전형절차는 직무별로 다르게 운영될 수 있으며, 일정 및 상황에 따라 변동될 수 있습니다.전형일정 및 결과는 지원서에 등록하신 이메일로 개별 안내드립니다.Resume Screening - Coding Test - Virtual Interview (approximately 1 hour) - Onsite or Virtual Interview (approximately 3 hours) - Final OfferPlease note that the interview process may vary depending on the position and is subject to change based on scheduling and other circumstances.Interview schedules and results will be communicated individually via the email address provided in your application.Additional Information 모든 제출파일은 PDF 양식으로 업로드를 부탁드립니다.국가보훈대상자 및 취업보호대상자는 관계법령에 따라 우대합니다.장애인 고용촉진 및 직업재활법에 따라 장애인 등록증 소지자를 우대합니다.42dot은 의뢰하지 않은 서치펌의 이력서를 받지 않으며, 요청하지 않은 이력서에 대해 수수료를 지불하지 않습니다.Please upload all required documents in PDF format.Veterans and applicants eligible for employment protection will receive preferential consideration in accordance with applicable laws and regulations.In compliance with the Act on Employment Promotion and Vocational Rehabilitation for Persons with Disabilities, registered individuals with disabilities will receive preferential consideration.42dot does not accept unsolicited resumes from search firms. We will not pay any fees for resumes submitted without prior agreement.※ 지원 전 아래 내용을 꼭 확인해 주세요.※ Please make sure to review the information below before applying.42dot이 일하는 방식, 42dot Way 보러가기 →Learn more about how we work at 42dot, 42dot Way →42dot만의 업무몰입 프로그램, Employee Engagement Program 보러가기 →Explore 42dot’s unique Employee Engagement Program, Employee Engagement Program →

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Do you get excited when your software actually interacts with the physical world? Are you ready to learn the new exciting world of smart robots? Do you want to build groundbreaking technology? Would you like to cooperate with top professionals in our industry?     If your answers are mostly yes, then you should keep reading.    At Nomagic, we’re on a mission to teach robots the real world. We’re now looking for a Machine Learning Intern, who’s ready to deep dive into the hard problems of physical manipulation (trying to match with software the millions of years of development leading to the human hand + eye) and bring his energy and commitment to our amazing team.Offer essentials: Play with robots every day Salary: 9 000 - 11 000 gross per month (umowa zlecenie) Truly flexible working hours Opportunity to learn from top-notch engineers English-speaking environment Partly remote work is possible! Here is why we love this job ourselves, and hope you will enjoy it too: We are at the forefront of AI Robotic systems changing our society - for the better!  We already have robots in production running 24/7 We are here to change the world. Our motto is “Iterate to excellence” We’re still pretty small, so everyone has a direct impact on the final result We have a very experienced AI, engineering & management team  We combine world-class research with top notch engineering and apply it to solve real problems Some of the problems you may try to solve with us: Expanding the perception capabilities of our system by enabling more variety of products that our robots can handle Detecting anomalies, e.g. using a combination of signals to determine if a robot picked more than one item at once or if one item is disassembling Training models that solve multiple problems with multiple loss functions Productionizing ML models - performance monitoring and AB-testing What skills we’d like you to have: Hands-on experience with deep learning models Understanding of fundamentals of machine learning and deep learning Great problem solving and proficiency in Python Ability to learn quickly new domains and navigate in complex systems Energy and hands-on attitude! Strive to get things done and find solutions whatever it takes Fluent communication in English Final year student or recent graduate (We plan you will stay with us) What should you expect once you apply? Codility test 60 minutes coding interview 60 minutes machine learning interview See a short sneak peak of our product here: https://youtu.be/RQv_rbU8ffw   To apply, please click on the button below.

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Product Engineer — SearchYou'll own the developer-facing search experience at Firecrawl — taking the retrieval and ranking improvements coming out of research and shipping them as a product developers can't stop using. This isn't a pure research role and it isn't a pure backend role. You sit at the intersection: you understand how the systems work deeply enough to improve them, and you care about how they feel to use obsessively enough to make them great. At a 26-person company, the gap between research and shipped product is exactly one person. You're that person.Salary Range: $180,000–$290,000/year (Range shown is for U.S.-based employees. Compensation outside the U.S. is adjusted fairly based on your country's cost of living. You can explore how we calculate this here: https://www.firecrawl.dev/careers/compensation.)Equity Range: Up to 0.15%Location: San Francisco, CA or Remote (Americas, UTC-3 to UTC-10)Job Type: Full-TimeExperience: 3+ years in applied RL, ML engineering, or model training — with production systemsVisa: US Citizenship/Visa required for SF; N/A for RemoteAbout FirecrawlFirecrawl is the easiest way to extract data from the web. Developers use us to reliably convert URLs into LLM-ready markdown or structured data with a single API call. In just a year, we've hit 8 figures in ARR and 100k+ GitHub stars by building the fastest way for developers to get LLM-ready data.We're a small, fast-moving, technical team building essential infrastructure superintelligence will use to gather data on the web. We ship fast and deep.What You'll DoShip search improvements that developers notice. Take retrieval and ranking improvements from research and turn them into product changes that make developers say "this just works." You know that a 200ms latency improvement isn't just a benchmark win — it's a better product. You ship the whole thing: the API change, the docs update, the example that makes it obvious.Own the search API end-to-end. You're responsible for how Firecrawl's search endpoint feels to integrate, use, and build on. That means response format, latency, error handling, pagination, filtering — every surface a developer touches. You're the person who notices when something is confusing before a user files a GitHub issue about it.Dogfood relentlessly. You build things with the API before you ship them. You feel the friction before your users do. You read every GitHub issue, every Discord thread, every support ticket that touches search — not because someone asked you to, but because that's where the product signal lives.Translate research into product decisions. You work closely with the Search/IR and RL Research Engineers. You understand their work well enough to make good product calls about what to prioritize, what to expose to users, and what to keep under the hood. You ask good questions. You push back when something technically elegant would make the API worse.Run fast product experiments. You form a hypothesis about what would make search better for developers, instrument it, ship it, measure it, and decide quickly. You're comfortable making calls with imperfect data because waiting for perfect data means shipping nothing.Raise the bar on developer experience. Firecrawl's users are technical. They have high standards. They notice when response formats are inconsistent, when error messages are unhelpful, when documentation doesn't match behavior. You notice too — and you fix it before they have to ask.What We're Looking ForObsessive about developer experience. You think about DX the way a designer thinks about pixels. Latency, response structure, error messages, API ergonomics — these things matter to you on a visceral level. You've built APIs that developers loved and you know the difference between an API that works and one that delights.Speaks both product and engineering fluently. You can read a ranking algorithm and understand its implications for the search experience. You can write the API spec and implement it yourself. You don't need a PM to tell you what matters or an ML engineer to explain why a retrieval change is significant. You connect those dots on your own.Hands-on builder who ships. You write code. You own features from design to deployment. You're comfortable with ambiguity and you don't need a perfectly scoped ticket to make progress. You ship something, learn from it, and iterate.Has a feel for search as a product. You've thought seriously about what makes search good — not just fast or accurate, but genuinely useful. You understand the difference between recall and precision and why developers care about both. You have intuitions about query understanding, result ranking, and when semantic search beats keyword search — and you've built products that put those intuitions to work.Brings production instincts. You've operated systems under real load. You know what breaks first, how to instrument what matters, and how to make good latency/quality tradeoffs. You're not just building features — you're building infrastructure developers depend on.Backgrounds that tend to do well: Engineers who've owned search or discovery features at developer-tools companies. Full-stack engineers with a strong backend bias who've shipped APIs used by thousands of developers. Engineers from search infrastructure teams who got frustrated by the distance between their work and the user experience. People who've built on top of Elasticsearch, Vespa, or vector databases — and cared enough about the product layer to go deeper than the query interface.What We're NOT Looking ForGreat engineers who don't care about DX. If you build technically excellent systems but think API ergonomics and documentation are someone else's problem, this isn't the role. The product experience is part of the job — not an afterthought.People who need a PM. There's no product manager between you and the work. You define what good looks like, you decide what to prioritize, and you own the outcome. If that's uncomfortable, you'll struggle here.Specialists who only work on one layer. If you're only interested in backend systems and tune out when the conversation shifts to how something is exposed to developers — or vice versa — this won't be a fit. This role requires you to hold both.Slow shippers. The research team will produce improvements faster than a slow product cycle can absorb them. We need someone who can take something from "this ranking model is better" to "this is live in the API with docs and an example" in days, not sprints.People who don't use the product. If you're not the kind of engineer who builds side projects with APIs like ours, reads the docs critically, and notices when something feels off — you'll miss the signal that makes this role work.A Note On PaceWe operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.Benefits & PerksAvailable to all employeesSalary that makes sense — $180,000–$290,000/year, based on impact, not tenureOwn a piece — Up to 0.15% equity in what you're helping buildGenerous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to rechargeParental leave — 12 weeks fully paid, for moms and dadsWellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you humanLearning & Development — Expense up to $1,000/year toward anything that helps you grow professionallyTeam offsites — A change of scenery, minus the trust fallsSabbatical — 3 paid months off after 4 years, do something fun and newAvailable to US-based full-time employeesFull coverage, no red tape — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that worksLife & Disability insurance — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballsSupplemental options — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mindDoctegrity telehealth — Talk to a doctor from your couch401(k) plan — Retirement might be a ways off, but future-you will thank youPre-tax benefits — Access to FSAs and commuter benefits (US-only) to help your wallet out a bitPet insurance — Because fur babies are family tooAvailable to SF-based employeesSF HQ perks — Snacks, drinks, team lunches, intense ping pong, and peak startup energyE-Bike transportation — A loaner electric bike to get you around the city, on usInterview ProcessApplication Review — Send us your work and a quick note on why this excites you. Show us what you've shipped — search features, APIs, developer-facing products. A GitHub link, a product you've built, or a write-up of something you're proud of goes a long way.Intro Chat (~20 min) — A quick conversation to get to know each other before we go deep. We'll talk about what you've been working on, what drew you to Firecrawl, and what you're looking for in your next role. Time for your questions too.Technical Deep Dive (~60 min) — Go deep on search products and APIs you've built: architecture decisions, DX tradeoffs, how you've translated technical improvements into product changes. We'll explore a live problem — how you'd take a retrieval improvement and ship it as a better developer experience at Firecrawl. We're looking for product instincts, technical depth, and the ability to hold both at once.Founder Chat (~30 min) — Culture, pace, ownership, and how you like to work. Time for your questions too.Paid Work Trial (1–2 weeks) — Tackle a real search product problem with production implications. We evaluate on shipping speed, product judgment, and how well you balance technical quality with developer experience.Decision — We move fast after the trial.If you want to own the search experience at one of the fastest-growing developer infrastructure companies in AI — and you're the kind of engineer who won't stop until it's great — this is your shot.👉 Apply now.

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We are looking for a New Grad, Full Stack Engineer to become part of our mission focused and high performing engineering team, and work closely with colleagues in Ankara, Berlin and our Silicon Valley Headquarters, to drive meaningful AI product innovation at massive scale.The Mission: TrustLab is building the trust and safety layer that enables AI agents to be deployed responsibly, as well as other scalable AI solutions, at global scale. We develop and deploy advanced autonomous AI systems that monitor, evaluate and observe agents during development and in production. Founded by senior leaders from Google, YouTube, TikTok, and Reddit, TrustLab’s solutions are industry leading and used by global technology brands to support their daily operations.The Impact: You are part of a team that is responsible for some of the most critical components of our AI centric tech stack. You contribute to building parts of our global platform that evaluates cutting edge customer AI systems at scale. You will work closely with experienced engineers to push the boundaries of what’s possible with state of the art AI tech, while learning and growing in a high-impact environment.The Role We’re looking for a Backend Engineer to help build high-performance backend systems that support AI-driven workflows and large-scale data processing. You’ll work on distributed services, APIs, and core infrastructure powering mission-critical products. This role requires strong backend fundamentals, Python experience, and a willingness to learn how to think in systems, not just endpoints.What You’ll DoBuild and maintain backend services in PythonContribute to APIs and microservices with clean, maintainable abstractionsWork with datasets and asynchronous processing pipelinesImprove performance, reliability, and observabilityParticipate in system design discussions and learn from senior engineersCollaborate closely with frontend and AI/ML engineersWrite production-quality, testable, secure codeParticipate in code reviews and continuously improveWhat We’re Looking ForCore Requirements0–2 years of backend engineering experienceExperience with Python (FastAPI, Django, Flask, or similar)Basic understanding of REST APIs and backend development principlesFamiliarity with databases (PostgreSQL, MySQL)Eagerness to learn system design, scalability, and performanceBonus points:Exposure to AWS or another cloud providerInterest in AI/ML systems or data-intensive applicationsHow We Work:Small teams, high ownership, minimal process overheadAsync-first collaboration across Ankara and Silicon ValleyEngineers are involved in product and technical decisions, not just executionWhy Join TrustLab Ankara? Foundry of Experts: Work directly with AI Industry leaders who scaled trust & safety systems at the world’s largest platforms. Competitive Compensation: Annual salary range of $40k–$45k USD, plus meaningful equity in a venture-backed startup. Modern Workspace: Hybrid flexibility with in-person collaboration at our Maidan, Ankara office. Substantial benefits incl. paid supplemental private health insurance, education stipend, meals and more. Annual Paid US Offsite: Opportunity to join the full team at our US headquarters for strategy alignment, deep collaboration, and innovation.

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About Xaira Therapeutics Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London.AI in Residence AI in Residence is a highly selective role at the intersection of frontier machine learning and drug discovery. Designed as an industry alternative to a traditional postdoctoral position, the program is for exceptional researchers and engineers who want to apply advanced AI to real biomedical problems end to end, from data to deployed systems. Residents join a small cohort working on high-impact AI efforts across Xaira. You’ll collaborate closely with AI scientists, research engineers, and drug discovery teams to design, build, and ship machine learning capabilities that directly influence therapeutic programs. This is hands-on, system-level work with real scientific consequence. We’re looking for candidates with technical depth, intellectual independence, strong research judgment, and evidence of delivering high-quality work—whether through publications, open-source, or production systems. What You’ll Do Develop and advance ML models for biological, preclinical, and translational datasets (e.g., multimodal omics, imaging, text, assay data) Design and implement scalable pipelines for data curation, training, evaluation, and inference integrated into discovery workflows Own projects end-to-end: problem framing → prototyping → validation → deployment Evaluate robustness and reliability (generalization, uncertainty, failure modes), plus interpretability where it supports scientific decision-making Contribute technical leadership by proposing new directions, shaping platform capabilities, and raising engineering/research standards through collaboration You Might Work On Examples include (not limited to): Foundation / representation models over multimodal biological and translational data Methods for small, biased, noisy datasets; distribution shift; and uncertainty estimation ML systems for experimental prioritization, assay interpretation, or translational signal discovery Evaluation frameworks and benchmarks tailored to discovery decision-making Tooling that makes models usable by scientists (interfaces, automation, monitoring) What Success Looks Like You ship one or more models or pipelines that are used in real discovery workflows Your work improves decision quality (e.g., better prioritization, faster iteration, clearer uncertainty) You raise the bar on evaluation rigor and reproducibility (strong baselines, error analysis, reliable metrics) You leave behind maintainable systems (tests, documentation, monitoring) that others can build on We Value Strong research judgment: choosing the right problems and knowing what “good evidence” looks like Rigor: careful experimental design, ablations, error analysis, and honest reporting Systems thinking: reliability, scalability, and maintainability—not just prototypes Clear communication: writing, documentation, and sharing decisions/assumptions Collaborative execution with scientific and engineering partners Program Structure Duration 6–12 months, with the possibility of extension or conversion to full-time Start Dates First hires beginning March 2026, with rolling applications and additional intakes in Summer and Fall 2026 Cohort Size Small, highly selective cohort to enable meaningful ownership and close collaboration Mentorship & Support Dedicated technical mentor, plus structured feedback from senior AI, engineering, and scientific leadership Publications & Presentations We value scientific contribution and may support publications and conference presentations when appropriate. Publication scope and timing depend on project needs and are subject to internal review (e.g., IP and confidentiality). Authorship follows standard contribution-based guidelines. Who Should Apply We encourage applications from candidates who meet most of the following: Recent MS or PhD graduates (or equivalent research experience) in ML/AI, computational biology, biomedical engineering, or related fields Evidence of research excellence through high-quality publications or artifacts. Top venues (e.g., NeurIPS, ICML, ICLR, CVPR, ACL; Nature Methods, Cell Systems) are a plus, but strong preprints, open-source contributions, or shipped systems with demonstrated impact are equally compelling Demonstrated ability to lead substantial technical work with originality—new modeling ideas, rigorous experiments, or production-grade systems adopted by others Motivation to translate rigorous research into reliable, deployable AI systems that support therapeutic discovery Please include a brief cover letter describing your interest in this role, why you’re excited about this area, and what you hope to gain from the experience. Compensation: The expected monthly compensation range is $10,000–$15,000, depending on experience and qualifications. We are open to higher compensation for candidates with exceptional experience or impact.

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(Fully remote, async-first, DevTool SaaS, 32-40h/week, time zones: UTC-3 to UTC+3)Location: This is a FULLY remote role, but you must be within UTC-3 to UTC+3 to work with your team, peers, and internal customers. You do not have to be in the specific country or city shown in this listing, but please only apply if you are physically based within the UTC-3 to UTC+3 timezone.--Join Checkly as a Senior Product Engineer AI and empower developers and agents to own and ensure application performance and reliability - from pull request to post-mortem!Checkly helps engineers build reliable products by unifying testing, synthetic monitoring and observability. OpenTelemetry, Playwright, and Monitoring as Code are our foundation for unifying performance and reliability.Our tech stack is Vue.js, Node.js, TypeScript, Postgres, ClickHouse and AWS. We ship to prod all day, every day, including our agentic layer, which we just released. We practice Agile (not scrum) and take pride in good documentation and good looking products.We’re a remote-first startup that keeps things simple—low on meetings, high on productivity, and always shipping. If you love building cool things, working async, actually listening to and working with customers, and keeping it lean, you’ll fit right in!Thousands of developers—teams at 1Password, CrowdStrike, Render, LinkedIn and Vercel included—trust Checkly to keep their apps running smoothly. In 2024, we raised $20M in Series B funding from Balderton, CRV, and Accel to take things to the next level.We care about building a team where people of all backgrounds are encouraged to do their best work. To achieve this we built a flexible, remote-first and async-first startup environment with inclusive benefits and full transparency about how we pay.What you'll doDesign & build AI agents and AI-enhanced features iteratively that help our customers debug, fix and create Playwright and API tests faster.Implement solutions full stack with your team.Get in touch with users to learn from their feedback directly, we want to build solutions that are delightful and solve real problems.What you should haveExcitement to build AI solutions that help developers debug, test, and ship faster.A track record of building AI solutions with customer focus.Experience in building applications in a professional product (SaaS) environment ideally with JS/TS & Node.js.You have the ability to be autonomous and self-motivated in a remote work environment, while you also enjoy getting to know your colleagues and helping others.You assume good intentions and are a great communicator in spoken and written English.You love making software!How we workTeams organize around outcomes, not ceremonies. We ship and iterate continuously.You work autonomously and own your output.We talk to customers. All interactions matter.We assume good intentions, give direct feedback, and do not avoid uncomfortable conversations.Why us?Have business impact and the instant visibility that a startup offers.Transparent salary because your salary shouldn't be dictated by how good a negotiator you are. (more info below)Remote-first, flexible work hours, async-first (low meeting, high productivity) and transparent cultureStock options27 days of paid vacation + your local public holidaysPaid sick leave & up to 14 weeks of paid parental leave$1,500 learning, visiting and wellbeing budgetCo-working budget or home office setupAnnual company retreatsFind out more here.What we payFair, competitive, and transparent pay is very important for us. Therefore we work with a standardized salary calculator that includes several factors such as seniority level as well as location.For this role, the range is€93k - €103k for someone in a similar cost of market as UK, Germany etc.€84k - €93k for someone in a similar cost of market as Spain, Poland, Ukraine etc.ApplyIf this sounds interesting, please apply! Studies by several different sources have shown that on average men will apply for a job if they meet 60% of the application requirements. Women, non-binary people and POC will seek to match a much higher percentage of requirements before applying. If you're not sure you're the right fit, apply anyway and let us know what you bring to the table. We'd love to hear from you!We're all about being transparent and setting clear expectations. That's why we've put together our hiring playbook and open-sourced our employee handbook. There you'll find a sneak peek of who we are, how we work and what you can expect in our hiring process.

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Please submit your resume in English and indicate your level of English proficiency.Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation isproject-based, not permanent employment.What this opportunity involves As an AI Trainer - Junior Journalist, your work will help train AI models, shaping how they understand and generate human-like text. This isn’t just traditional writing—you’ll be crafting text that teaches AI reasoning, logic accuracy, nuance, and clarity. While each project involves unique tasks, contributors may:Craft original, clear, and fact-checked responses based on project guidelines. Generate prompts that challenge AI. Define comprehensive scoring criteria to evaluate the accuracy of the AI’s answers. Follow style and quality standards to ensure consistency.What we look forThis opportunity is a good fit if you are seeking for open to part-time, non-permanent projects. Ideally, contributors will have:  Bachelor’s degree in Journalism, Communications, Linguistics, Literature, or Education to ensure a strong understanding of grammar and stylistic features; At least 1 year of professional experience in Journalism, Communications, PR, etc., with strong skills in critical thinking and working with text in English language; Strong written English (C1/C2); Stable internet connection. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidProject time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. CompensationOn this project, contributors can earn up to $20 per hour equivalent, depending on their level and pace of contribution.Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

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Please submit your resume in English and indicate your level of English proficiency.Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation isproject-based, not permanent employment.What this opportunity involves As an AI Trainer - Junior Journalist, your work will help train AI models, shaping how they understand and generate human-like text. This isn’t just traditional writing—you’ll be crafting text that teaches AI reasoning, logic accuracy, nuance, and clarity. While each project involves unique tasks, contributors may:Craft original, clear, and fact-checked responses based on project guidelines. Generate prompts that challenge AI. Define comprehensive scoring criteria to evaluate the accuracy of the AI’s answers. Follow style and quality standards to ensure consistency.What we look forThis opportunity is a good fit if you are seeking for open to part-time, non-permanent projects. Ideally, contributors will have:  Bachelor’s degree in Journalism, Communications, Linguistics, Literature, or Education to ensure a strong understanding of grammar and stylistic features; At least 1 year of professional experience in Journalism, Communications, PR, etc., with strong skills in critical thinking and working with text in English language; Strong written English (C1/C2); Stable internet connection. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidProject time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. CompensationOn this project, contributors can earn up to $30 per hour equivalent, depending on their level and pace of contribution.Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

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Please submit your resume in English and indicate your level of English proficiency.Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation isproject-based, not permanent employment.What this opportunity involves As an AI Trainer - Junior Journalist, your work will help train AI models, shaping how they understand and generate human-like text. This isn’t just traditional writing—you’ll be crafting text that teaches AI reasoning, logic accuracy, nuance, and clarity. While each project involves unique tasks, contributors may:Craft original, clear, and fact-checked responses based on project guidelines. Generate prompts that challenge AI. Define comprehensive scoring criteria to evaluate the accuracy of the AI’s answers. Follow style and quality standards to ensure consistency.What we look forThis opportunity is a good fit if you are seeking for open to part-time, non-permanent projects. Ideally, contributors will have:  Bachelor’s degree in Journalism, Communications, Linguistics, Literature, or Education to ensure a strong understanding of grammar and stylistic features; At least 1 year of professional experience in Journalism, Communications, PR, etc., with strong skills in critical thinking and working with text in English language; Strong written English (C1/C2); Stable internet connection. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidProject time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. CompensationOn this project, contributors can earn up to $20 per hour equivalent, depending on their level and pace of contribution.Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

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Please submit your resume in English and indicate your level of English proficiency.Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation isproject-based, not permanent employment.What this opportunity involves As an AI Trainer - Junior Journalist, your work will help train AI models, shaping how they understand and generate human-like text. This isn’t just traditional writing—you’ll be crafting text that teaches AI reasoning, logic accuracy, nuance, and clarity. While each project involves unique tasks, contributors may:Craft original, clear, and fact-checked responses based on project guidelines. Generate prompts that challenge AI. Define comprehensive scoring criteria to evaluate the accuracy of the AI’s answers. Follow style and quality standards to ensure consistency.What we look forThis opportunity is a good fit if you are seeking for open to part-time, non-permanent projects. Ideally, contributors will have:  Bachelor’s degree in Journalism, Communications, Linguistics, Literature, or Education to ensure a strong understanding of grammar and stylistic features; At least 1 year of professional experience in Journalism, Communications, PR, etc., with strong skills in critical thinking and working with text in English language; Strong written English (C1/C2); Stable internet connection. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidProject time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. CompensationOn this project, contributors can earn up to $16 per hour equivalent, depending on their level and pace of contribution.Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

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Please submit your resume in English and indicate your level of English proficiency.Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation isproject-based, not permanent employment.What this opportunity involves As an AI Trainer - Junior Journalist, your work will help train AI models, shaping how they understand and generate human-like text. This isn’t just traditional writing—you’ll be crafting text that teaches AI reasoning, logic accuracy, nuance, and clarity. While each project involves unique tasks, contributors may:Craft original, clear, and fact-checked responses based on project guidelines. Generate prompts that challenge AI. Define comprehensive scoring criteria to evaluate the accuracy of the AI’s answers. Follow style and quality standards to ensure consistency.What we look forThis opportunity is a good fit if you are seeking for open to part-time, non-permanent projects. Ideally, contributors will have:  Bachelor’s degree in Journalism, Communications, Linguistics, Literature, or Education to ensure a strong understanding of grammar and stylistic features; At least 1 year of professional experience in Journalism, Communications, PR, etc., with strong skills in critical thinking and working with text in English language; Strong written English (C1/C2); Stable internet connection. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidProject time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. CompensationOn this project, contributors can earn up to $18 per hour equivalent, depending on their level and pace of contribution.Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.