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. We believe the fastest way to reach AGI lies in accelerating software development itself, by reshaping the developer experience with agentic systems, coding assistants, and the frontier models that power them. We deploy these systems directly into the development environments of security-conscious enterprises.

ABOUT OUR TEAM

We were founded in the US and have our home there, but our team is distributed across Europe and North America. We get our fix of in-person collaboration (and croissants) in Paris each month for 3 days, always Monday-Wednesday, with an open invitation to stay the whole week. We also do longer off-sites once a year.

Our team is a multidisciplinary blend of research, engineering, and business experts. What unites us is our deep care for what we build together. We’re in a race that requires hard work, intellectual curiosity, and obsession; to balance this intensity, we’ve assembled a team of low ego and kind-hearted individuals who have built the special culture Poolside has. By building collaboratively and with intention, we create a compounding effect that moves the entire company forward towards our mission: reaching AGI through intelligence systems built for software development.

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 insurance allowance for you & dependents
16 weeks of flexible, full-pay parental leave
Well-being, always-be-learning & home office allowances
Company-provided equipment
Frequent team get togethers
Diverse & inclusive people-first culture

Member of Engineering (Reinforcement Learning Infrastructure)