Research Engineer, Pretraining Scaling
Research Engineer optimizes and scales production pretraining of frontier AI models, handling performance, debugging, experiments, and on-call incidents. Requires expertise in JAX, TPU, PyTorch, or large-scale ML systems with a 50/50 research-engineering balance.
About the job
Responsibilities
- Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability
- Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
- Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
- Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
- Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
- Add new capabilities to the training codebase, such as long context support or novel architectures
- Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
- Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned
You May Be a Good Fit If You
- Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
- Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50
- Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure
- Thrive when working on whatever is most impactful, even if that changes day-to-day
- Excel at debugging complex, ambiguous problems across multiple layers of the stack
- Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents
- Are passionate about the work itself and want to refine your craft as a research engineer
- Care about the societal impacts of AI and responsible scaling
Strong Candidates May Also Have
- Previous experience training LLMs or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
- Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)
- Published research on model training, scaling laws, or ML systems
- Experience with production ML systems, observability tools, or evaluation infrastructure
- Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence
Location: This role requires working in-office 5 days per week in San Francisco.
Skills
JAX, Tpu, PyTorch, LLMs, Distributed Systems, Ml Systems, Observability Tools, Kubernetes, Monitoring Dashboards, Evaluation Infrastructure
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