Reinforcement Learning Infrastructure Engineer

Jobtailor; Palo Alto (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Jobtailor in Palo Alto, CA seeks an experienced infrastructure engineer to design and optimize large-scale reinforcement learning and post-training workloads. You will collaborate with researchers and engineers to translate ideas into production-grade training pipelines and to improve GPU utilization across the cluster.

This role focuses on building reliable, scalable RL training infrastructures, enhancing monitoring, and ensuring high throughput with multi-node orchestration using modern

Qualifications

• 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.

Responsibilities

• 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

Skills

Distributed systems

Actor-learner

Python

PyTorch

JAX

Async training

Replay buffers

Simulation env

Ray

Kubernetes

SLURM

GPU throughput

Debugging

Tools

Ray

SLURM

Kubernetes

Job description

• 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

• 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