RL Infrastructure Engineer for Scalable GPU Training

Elorian AI; San Francisco (CA); On-site; USD 200,000 - 400,000; Full time

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Elorian AI, an on-site AI research lab in Palo Alto, is seeking an infrastructure engineer to design and build core systems for RL training pipelines. You will own end-to-end training infrastructure, from rollout to observability, partnering with researchers to translate ideas into production-grade pipelines.

You will design scalable RL training, optimize GPU utilization, and develop monitoring tools to ensure reliability.

Qualifications

• 3+ years of distributed systems experience.
• 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 environments.
• Multi-node GPU orchestration experience (Ray, SLURM, or Kubernetes).
• A track record of improving training throughput and GPU utilization at scale.
• Strong engineering skills; maintainable code and debugging in large codebases.

Responsibilities

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

Skills

Distributed systems

Actor-learner architectures

Python

Code quality

Debug complex codebases

Tools

PyTorch

JAX

Ray

SLURM

Kubernetes

Job description

Elorian AI, an on-site AI research lab in Palo Alto, is seeking an infrastructure engineer to design and build core systems for RL training pipelines. You will own end-to-end training infrastructure, from rollout to observability, partnering with researchers to translate ideas into production-grade pipelines.

You will design scalable RL training, optimize GPU utilization, and develop monitoring tools to ensure reliability.