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The role involves researching and developing new pre-training methodologies for AI, requiring a blend of theoretical exploration and practical engineering. Responsibilities include designing experiments, collaborating on large-scale research, and publishing findings.

The Post-Training Researcher develops post-training recipes, iterates evaluations, debug training configurations, and publishes research to advance AI knowledge.

The role requires designing data collection strategies, developing pipelines for labeling and data generation, and researching human preferences to guide model behavior. Responsibilities include evaluating data quality and scalability, as well as publishing research to advance the AI community.

Build and ship Tinker platform features across frontend, backend, and infrastructure. Implement backend services and APIs in Python and Rust, user-facing UIs with React/TypeScript, improve SDK/CLI developer experience, enhance reliability and observability, participate in on-call, and create internal tooling to support ML fine-tuning workflows and platform operations.

Develop and maintain full stack applications focusing on APIs, UX, system reliability, security, and collaboration across teams.

Lead a team of engineers to design and scale reliable ML systems, ensure high execution standards, and collaborate with tech leads and product teams.

The HR Business Partner will coach managers, advise on organizational decisions, design compensation structures, and build evaluation processes to support employee growth and performance.

Coordinate high-volume interview scheduling across time zones, manage logistics (travel, expenses, rooms, technical support), own candidate communications, scale and train interviewer pools, use scheduling tools to automate processes, collect and audit interview feedback, maintain recruiting documentation and data integrity, and provide ad-hoc operational support for recruiting initiatives.

Build, operate, and maintain research infrastructure (evaluation frameworks, RL training systems, experiment tracking, visualization). Develop scalable distributed pipelines, ensure reproducibility and observability, and partner with researchers and infrastructure teams to accelerate ML research and tooling adoption.

Build and operate ML research infrastructure: evaluation frameworks, RL training systems, experiment tracking, distributed evaluation pipelines, observability, and reproducibility tooling. Partner with researchers to identify bottlenecks, drive adoption, and integrate tools across infrastructure, data, and product stacks.