Member of Engineering – Reinforcement Learning Infrastructure

🕒 April 28

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poolside

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2023

🤖 Artificial Intelligence

🏢 Enterprise

Artificial Intelligence • Enterprise

poolside is a frontier AI lab and enterprise platform that builds and deploys foundation models, multi-agent systems, and developer-facing tools focused on automating complex software work. The company specializes in on-prem and VPC deployments, security-first integrations, governance, and connectors to enterprise data sources so organizations can run agents and models inside their own boundaries. Poolside embeds research and engineering with customers to deliver outcome ownership, risk controls, and measurable business impact while advancing toward AGI by starting in high-consequence software environments.

📋 Description

• 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

🎯 Requirements

• 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

🏖️ Benefits

• Fully remote work & flexible hours • 37 days/year of vacation & holidays • Health insurance allowance for you and dependents • Company-provided equipment • Wellbeing, always-be-learning and home office allowances • Frequent team get togethers • Great diverse & inclusive people-first culture