Research Engineer, Forge

Job summary

Responsibilities

As a Research Engineer on Forge, you will turn real customer requirements into reliable training and deployment workflows. You’ll work end‑to‑end across model adaptation and post‑training (CPT/SFT/RL/distillation), evaluation, data, and infrastructure.

• Build and improve post‑training and evaluation workflows (CPT/SFT/RL/distillation), turning prototypes into repeatable Forge “recipes”.
• Develop tools and pipelines for synthetic data generation, data curation, training, evaluation, and deployment.
• Debug and harden large‑scale ML systems: distributed training, scheduling/execution, checkpointing, observability, and reproducibility.
• Improve the Forge codebase via clear APIs, tests, documentation, and maintainable abstractions.
• Push the frontier of our RL training stack (e.g., high-throughput async rollout and scalable post‑training systems at frontier-model scale)

What they're looking for

• Strong Python engineering skills and experience working in large codebases (testing, code review, CI, operational ownership).
• Hands‑on experience with PyTorch, JAX, or similar.
• Strong systems and infrastructure fundamentals.
• Experience with LLM training or post‑training: fine‑tuning, RL, distillation, evaluation, and/or data pipelines.
• Excellent debugging skills in ambiguous systems (distributed jobs, data issues, quality regressions, infra failures).

Tech stack

• Python
• PyTorch
• JAX
• Ray
• Kubernetes