Member of Engineering (Reinforcement Learning Infrastructure)

Member of Engineering (Reinforcement Learning Infrastructure) ABOUT POOLSIDE In this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers. poolside exists to be this company - to build a world where AI will be the engine behind economically valuable work and scientific progress. View GDPR Policy https://poolside.ai/gdpr ABOUT OUR TEAM We are a remote-first team that sits across Europe and North America and comes together once a month in-person for 3 days and for longer offsites twice a year. Our R&D and production teams are a combination of more research and more engineering-oriented profiles, however, everyone deeply cares about the quality of the systems we build and has a strong underlying knowledge of software development. We believe that good engineering leads to faster development iterations, which allows us to compound our efforts. ABOUT THE ROLE You would be working on our reinforcement learning team focused on improving reasoning and coding abilities of Large Language Models through reinforcement learning. This is a hands-on role where you’ll work end-to-end from researching new exploration or training algorithms, to designing and scaling up RL environments, to implementing your ideas across the stack. You will have access to thousands of GPUs in this team. YOUR MISSION Build and scale the infrastructure that enables reliable, efficient training of Large Language Models with Reinforcement Learning at the frontier. RESPONSIBILITIES - Keep up with the latest research, and be familiar with the state of the art in LLMs, RL, and code generation - Develop methods for tuning training and inference end-to-end for high throughput - Design data control systems in an RL pipeline that govern what the model sees and when - Debug cases where infrastructure decisions are silently degrading learning dynamics - Build observability tooling that surfaces when a system-level issue is the root cause of a training regression - Help build robust, flexible and scalable RL pipelines - Optimize performance across the stack — networking, memory, compute scheduling, and I/O - Write high-quality, pragmatic code - Work in the team: plan future steps, discuss, and always stay in touch SKILLS & EXPERIENCE - Experience with LLMs and model post-training workflows - Understanding how Reinforcement Learning works and what its main bottlenecks are - Solid software engineering fundamentals (testing, code review, debugging complex systems) - Proficiency in Python with knowledge of concurrency, asynchronous programming, multiprocessing and performance optimization - Familiarity with deep learning frameworks (PyTorch or JAX) and RL workflows (rollouts, replay buffers, policy updates) - Experience designing and maintaining distributed RL training systems - Experience with large-scale LLM training infrastructure - Experience with profiling tools across the stack (e.g. py-spy) - Experience with inference stacks (e.g. vLLM) - Nice to have: Open-source contributions to RL or distributed ML projects PROCESS - Intro call with one of our Founding Engineers - Technical Interview(s) with one of our Founding Engineers - Team fit call with the People team - Final interview with one of our Founding Engineers BENEFITS - Fully remote work & flexible hours - 37 days/year of vacation & holidays - Health i ... (truncated, view full listing at source)