Reinforcement Learning Research Engineer

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Location - Remote (US Based)

A scaling, SOTA Generative AI Startup operating with a world class team (Founders have multiple prior exits) with talent from Open AI, IBM, MIT and several top orgs, focused on pioneering work and advancements in large language models (LLMs), code generation, and code translation. Their projects directly involve industry leading partners where they’re applying advanced AI to solve meaningful, practical challenges with real-world impact.

• Build and maintain robust distributed training systems using PyTorch and JAX
• Build and train production-ready reinforcement learning infrastructure
• Develop orchestration tools that manage complex workflows across large-scale AI model training and evaluation.
• Drive innovation by researching and developing scalable reinforcement learning (RL) algorithms and training paradigms for complex, high-dimensional optimization and decision-making tasks, including recent advancements in RL for feedback-driven optimization in LLMs.
• Design and train large-scale RL environments for optimization problems spanning multiple industries.
• Engage with frontier research through open-source projects and potential publications.

• 2+ years of experience in distributed or decentralized RL (multi-agent preferred) using PyTorch and JAX.
• Research experience with RL for high-dimensional optimization problems, particularly in multi-agent reinforcement learning settings.
• Experience implementing advanced RL techniques such as task decomposition, hierarchical RL, goal-conditioned RL, or human-AI collaboration.
• Experience deploying and managing multi-GPU training infrastructure at scale.
• Eligible for TS/SCI clearance.

Get in touch today for more details and immediate consideration / interview!

Mid-Senior level

Full-time

Research and Engineering

Software Development and Research Services

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