Reinforcement Learning Infrastructure Engineer

Elorian AI

Reinforcement Learning Infrastructure Engineer for AI lab designing and optimizing large-scale RL training infrastructure. Collaborating with research teams to enhance training reliability and performance.

Posted 7/27/2026full-timePalo Alto • California • 🇺🇸 United StatesMid-LevelSenior💰 $200,000 - $400,000 per yearWebsite

Tech Stack

Tools & technologies

Distributed SystemsKubernetesNode.jsPythonPyTorchRay

About the role

Key responsibilities & impact

Design, build, and optimize the infrastructure that powers our large-scale RL and post-training workloads
Improve the reliability, scalability, and throughput of distributed RL training pipelines
Build actor-learner architectures and orchestrate environment rollouts at scale
Develop monitoring and observability tools that ensure high uptime, debuggability, and reproducibility across RL systems
Collaborate with researchers to translate algorithmic ideas into production-grade training pipelines
Improve GPU utilization and training throughput across the cluster

Requirements

What you’ll need

3+ years of distributed systems experience, including building or optimizing large-scale RL training pipelines (PPO, GRPO, or similar on-policy methods)
Experience with actor-learner architectures and environment rollout orchestration at scale
Strong Python skills, plus PyTorch or JAX
Experience with async training infrastructure, replay buffers, or simulation-based environment frameworks
Multi-node GPU orchestration experience (Ray, SLURM, or Kubernetes)
A track record of improving training throughput and GPU utilization at scale
Strong engineering skills; ability to contribute performant, maintainable code and debug in complex codebases

Benefits

Comp & perks

health, dental, and vision benefits
unlimited PTO
paid parental leave
relocation support as needed