Reinforcement Learning Infrastructure Engineer

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.