Reinforcement Learning Infrastructure Engineer
Summary
Designs and builds scalable infrastructure for reinforcement learning (RL) training pipelines, including actor-learner architectures, reward systems, and distributed GPU orchestration, using Python, PyTorch/JAX, and tools like Ray or Kubernetes.
About Us
We are a well-funded, early-stage AI lab focused on building the next generation of frontier multimodal AI models. Founded by former DeepMind researchers, including Andrew Dai, who was previously a leader on Gemini. Our team currently consists of 20 world-class scientists and engineers. We recently raised $55M in seed funding from Striker Ventures, Menlo Ventures, Altimeter Capital, and NVIDIA. We are tackling some of the hardest problems in artificial intelligence, and we are growing fast.
The Role
We're looking for an infrastructure engineer to design and build the core systems behind how we train our models with reinforcement learning (RL).
You'll own the training infrastructure end to end, from rollout and reward pipelines to orchestration, reliability, and observability. The work spans both the algorithmic side of RL and the systems reality of running distributed training at scale, and you'll partner closely with our research team to keep RL training fast, stable, and dependable for the multimodal, visual reasoning models at the center of our work.
What You Will Do
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
What We're Looking For
Minimum 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
Preferred qualifications (strong candidates may have some, not all):
Experience with multimodal or agentic RL environments
Experience with RLHF or reward modeling pipelines
A self-directed builder who moves quickly and works across teams in an early-stage setting
Logistics
Location: This role is based on-site in Palo Alto, California.
Compensation: Depending on background, skills, and experience, the expected annual base salary range for this position is $275,000 - $475,000 USD, plus equity and benefits.
Visa sponsorship: We sponsor work visas. We can't promise every case will succeed, but for the right person we'll work through the process with you.
Benefits: We offer health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Elorian AI is an equal opportunity employer. We are committed to building a diverse team and inclusive environment.