Member of Engineering (Reinforcement Learning)

Member of Engineering (Reinforcement Learning) 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 To push the frontier of reasoning and coding capabilities of foundational models, via Reinforcement Learning. RESPONSIBILITIES - Research and experiment on ways to improve reasoning and code generation for LLMs. Own the full experiment life cycle from idea to experimentation and integration - Keep up with the latest research, and be familiar with the state of the art in LLMs, RL, and code generation. Translate research ideas into clean, reusable codebases that other researchers can build on - Design, analyze, and iterate on data generation and training of LLMs - Implement and iterate on RL training pipelines that scale reliably across domains - Diagnose training instabilities and failures, debug RL runs and propose mitigation methods - Write high-quality, reproducible and maintainable code SKILLS & EXPERIENCE - Experience with Large Language Models (LLM), including: - Understanding of the Transformer architecture and scaling laws - Mid-training and post-training techniques - Experience training reasoning and/or agentic models - Hands-on use of LLMs, with a sense of their capabilities and limitations - Reinforcement Learning experience - Solid grasp of Reinforcement Learning concepts and familiarity with modern algorithms - Experience developing distributed, large-scale RL pipelines from data creation to evaluations - Research experience - Scientific publications in any of the following topics: Reinforcement Learning, LLMs and reasoning models - Ability to discuss the latest research with sufficient level of detail - Is reasonably opinionated - Engineering skills - Strong machine learning, algorithm skills and engineering background - Experience with distributed training - Excellent programming skills in Python - Familiarity with a deep learning framework (Pytorch or JAX) 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 remot ... (truncated, view full listing at source)