Research Engineer, Infrastructure, RL Systems

5 days ago

H1B Sponsor

Base Salary

$350k - $475k/yr

Responsibilities

Design, build, and optimize infrastructure for large-scale reinforcement learning and post-training workloads.
Improve the reliability, scalability, and throughput of distributed reinforcement learning training pipelines.
Develop monitoring and observability tools that support uptime, debuggability, and reproducibility.
Collaborate with researchers to translate algorithmic ideas into production-grade training pipelines.
Build evaluation and benchmarking infrastructure for model helpfulness, safety, and factuality.
Share technical learnings through internal documentation, open-source libraries, or technical reports.

Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or a similar field.
Strong engineering skills, including the ability to write performant, maintainable code and debug complex codebases.
Understanding of deep learning frameworks and their underlying system architectures, including PyTorch or JAX.
Experience training or supporting language models with tens of billions of parameters or more is preferred.
Experience with reinforcement learning workloads such as PPO, DPO, RLHF, or reward modeling is preferred.
Background in high-performance or reliability engineering, distributed training frameworks, or cluster orchestration such as Kubernetes or Slurm is preferred.
Familiarity with monitoring and observability tools such as Prometheus, Grafana, or OpenTelemetry is preferred.
Contributions to large-scale machine learning research or infrastructure, open-source frameworks, or performance optimization efforts are preferred.