Research Intern, Inference (Fall 2026)
As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.
Member of Technical Staff (Machine Learning Engineer)
Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.
Warehouse Supervisor (Temporary)
Utilize proprietary software to provide accurate input and labels for healthcare and administration projects, ensuring high-quality data for AI model training. Deliver curated, high-quality data for scenarios involving patient care coordination, medical billing, administrative workflows, and healthcare operations. Collaborate with technical staff to support the training of new AI tasks and contribute to the development of innovative technologies. Assist in designing and improving efficient annotation tools tailored for healthcare and administration data. Select and analyze complex problems in healthcare and administration fields aligned with your expertise to enhance AI model performance. Interpret, analyze, and execute tasks based on evolving instructions, maintaining precision and adaptability.
Senior Data Scientist
As a Data Scientist at Legora, you will turn data into decisions by sitting close to the business and taking questions end-to-end: shaping the metric, modelling the data in dbt, running analysis, and making recommendations. You will pull in new data sources as needed, leveraging the underlying instrumentation and platform maintained by the data engineering team. Responsibilities include partnering with stakeholders across Product, Finance, GTM, Growth, and other areas to translate ambiguous questions into structured analyses and clear recommendations; defining key metrics, designing experiments or analyses to test them, and measuring impact; conducting deep-dive analyses on business-critical questions and proactively identifying new questions; modeling necessary data in dbt and collaborating with data engineering on scalability; building dashboards and reports that can be used broadly within the company; and helping shape the data team's operations as it scales, including setting standards, tooling, and ways of working.
Mid/Senior AI Cinematic Video Editor (Full Remote - Sweden)
The Mid/Senior AI Cinematic Video Editor is responsible for conceptualising scripts based on current production needs centered around existing AI characters, creating narrative-driven longform video content including stylized and explicit NSFW visuals focusing on storytelling, atmosphere, and visual coherence. They own and manage end-to-end AI video production workflows from ideation and prompting to generation, editing, and post-production. The role involves extensive work with ComfyUI pipelines, building, customizing, and optimizing node-based workflows for image and video generation. The editor uses tools such as Stable Diffusion (AUTOMATIC1111), ComfyUI, Runway, and other AI video platforms to produce high-quality visual sequences, maintaining consistent character appearance, style, and scene continuity across longer narratives using advanced techniques. They integrate motion graphic design and color correction to deliver cohesive final outputs and rapidly experiment with new AI models, tools, and techniques, incorporating them into production workflows and sharing skills with the team. The editor aligns with the Content Lead’s creative direction while maintaining autonomy in execution and technical decisions, continuously refining workflows for efficiency, scalability, and output quality.
Software Engineer (Brazil)
Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.
Software Engineer (AI Focus)
Architect and implement all AI and LLM features end-to-end for the web platform, ensure the utilization of the latest LLM features and agentic tools, work with technologies such as React, Node.js, AWS, Azure, and Kubernetes, take ownership of the codebase balancing high-quality code, security, and speed of development, and own and drive forward proof of concepts (PoCs) and solve broad technical challenges.
Product Manager (Agents)
Lead the Lovable agent end-to-end by owning quality, roadmap, and feedback loops to improve it. Represent the user by synthesizing findings on agent performance and behavior and communicating these to the team clearly. Run discovery processes including user interviews, competitive research, evaluation analysis, prompt experimentation, and messaging for new agent capabilities. Own the quality bar for agent outputs by driving evaluation infrastructure, monitoring regressions, and ensuring continuous improvement with every release. Scope features carefully to deliver the right functionality, validate through user feedback and metrics, and eliminate non-effective parts. Enable sales, support, and marketing teams with the necessary context to communicate new agent capabilities effectively. Initial projects include rebuilding the agent evaluation framework to catch regressions before release, discovering gaps in agent reliability and trust, and defining and shipping the first iteration of improved agent error recovery and communication.
Forward Deployed Engineer - ML
As a Forward Deployed ML Engineer, you will work hands-on with companies such as Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads on Modal's platform. You will contribute to open-source projects like SGLang and publish technical content showcasing Modal's capabilities across the AI stack. Collaboration with Modal's product and sales teams is expected, serving both as an engineer and a product stakeholder. You will build trusted relationships with technical leaders including CTOs, VPs of Engineering, and ML leads at frontier AI companies. Additionally, you will conduct technical demos, experiments, and proof-of-concepts to demonstrate Modal's performance advantages.
Senior ML Operations (MLOps) Engineer
The Senior ML Operations (MLOps) Engineer at Eight Sleep is responsible for introducing and implementing cutting-edge ML technologies, owning the design and operation of robust ML infrastructure including scalable data, model, and deployment pipelines to ensure reliable model delivery to production. They collaborate cross-functionally with R&D, firmware, data, and backend teams to ensure reliable and scalable ML inference on Pods. They optimize ML systems for cost, scalability, and performance across training and inference, and develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment. The role requires effective communication in a remote work environment.
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