Research Engineer

The role As a research scientist, you will design, implement, and optimize the large-scale training infrastructure that powers our frontier reinforcement learning stack. This is systems work at the edge of what's possible, training state-of-the-art models for our enterprise partners. Frontier systems are exciting but brittle, and require both performance and correctness to train models effectively. You'll work closely with researchers to make our RL stack reliable, fast, and capable of running for days without intervention. What you'll do Design and optimize our RL training and inference pipelines across large GPU clusters Build tooling and observability that lets researchers and customers inspect, profile, and debug training runs Implement systems with an eye toward how they affect ML (low precision numerics, distributed training edge cases, etc.) Partner with researchers to bring frontier post-training capabilities into production deployments What we're looking for Experience programming with and managing training jobs on large-scale GPU systems Fearlessness and curiosity to understand all levels of the training stack Bias toward fast implementation, paired with a high bar for reliability and efficiency Familiarity with open-weights models (architecture and inference) Background in reinforcement learning or integration of inference with RL training loops Strong candidates also have Experience with distributed training frameworks (PyTorch, JAX, DeepSpeed) Background in high-performance computing or working with large-scale clusters Contributions to open-source ML infrastructure Demonstrated technical creativity through published projects, OSS contributions, or side projects About us Applied Compute builds Specific Intelligence for the enterprise. We provide the continual learning infrastructure for companies to build agent workforces trained on proprietary data and institutional expertise. Our researchers and platform embed directly within customer environments to build custom evals, train models, and deploy agents that get better with use. Why we’re excited: We get to work at a rare intersection. Our product team builds the platform powering a new generation of digital coworkers. Our research team pushes the frontier of post-training and reinforcement learning. Our applied AI team sits side‑by‑side with customers as they ship agents into production. This combination of strong product, deep research, and boots on the ground is what we believe it takes to bring AI to the enterprise. We are product‑led, research‑enabled, and forward‑deployed. Who we are : We’re a team of engineers, researchers, and operators. Many of us are former founders. We've built RL infrastructure at OpenAI, data foundations at Scale AI, and systems at Together, Two Sigma, Watershed, and others. We work with F50 customers and are fortunate to be backed by partners like Kleiner Perkins, Benchmark, Sequoia, Lux, and Greenoaks. Who Thrives Here : We're looking for people who are excited about applying novel research and complex systems to real-world problems. Our team genuinely enjoys working with customers: listening, empathizing, and understanding how work actually gets done in their organizations. Former founders, people who've built a lot of side projects, or anyone who's shown they can own something end‑to‑end, tend to do well here. Benefits & Logistics This role is based in San Francisco. We work from our office in the Mission. We offer: Competitive compensation and equity Generous health benefits Unlimited PTO Paid parental leave Daily lunches and dinners Transportation and relocation support Retirement plans We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the process with you. We encourage you to apply even if you do not believe you meet every single qualification. As set forth in Applied Compute’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law. #J-18808-Ljbffr