RL Systems Engineer: Scale Training Pipelines & GPUs

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

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