Research Engineer, Infrastructure, RL Systems

What You’ll Do

• Design, build, and optimize the infrastructure that powers large-scale reinforcement learning and post-training workloads.
• Improve the reliability and scalability of RL training pipeline, distributed RL workloads, and training throughput.
• Develop shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility for RL systems.
• Collaborate with researchers to translate algorithmic ideas into production-grade training pipelines.
• Build evaluation and benchmarking infrastructure that measures model progress on helpfulness, safety, and factuality.
• Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases.
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications:

• Experience training or supporting large-scale language models with tens of billions of parameters or more.
• Experience working with reinforcement learning workloads (e.g., PPO, DPO, RLHF, or reward modeling).
• Background in high-performance or reliability engineering — distributed training frameworks and cluster orchestration (Kubernetes, Slurm).
• Familiarity with monitoring and observability tools (Prometheus, Grafana, OpenTelemetry).
• Contributions to large-scale ML research or infrastructure, open-source frameworks, or internal performance optimization efforts.

Logistics

Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.

Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.