Research Engineer / Scientist – Reinforcement Learning

🔥 18 minutes ago

🏄 California – Remote

🏄 California – Remote

💵 $150k - $350k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📚 Research Engineer

👻 Ghost score 0%

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Prime Intellect

WebsiteLinkedInAll Job Openings

1 - 10 employees

🤖 Artificial Intelligence

☁️ SaaS

Artificial Intelligence • SaaS • Cloud Computing

Prime Intellect is a company focused on democratizing AI development by providing scalable and decentralized computing resources for training models. Their platform allows users to find and share global compute resources, enabling the training of state-of-the-art models through distributed clusters. They promote the collective ownership of AI innovations, including language and scientific models. Prime Intellect also offers a range of GPU options to facilitate affordable and efficient model training. They aim to advance decentralized training research and open-source AI development on a global scale.

📋 Description

• Develop and test RL methods for reasoning, coding, and long-horizon tasks, including changes to training objectives and policy optimization • Investigate stability in asynchronous RL, including policy staleness, trainer/sampler mismatch, and numerical differences between training and rollouts • Work with data and evaluation researchers to understand how task selection, rewards, and training data affect what the model learns • Design experiments to understand how RL performance and learning dynamics change with model size, training compute, and task horizon • Use controlled ablations and evaluations to distinguish learning gains from reward exploitation, evaluation artifacts, or training bugs • Turn experimental findings into training recipes and test whether improvements hold across larger models and longer runs • Extend prime-rl to support new algorithms, models, and asynchronous training workflows • Improve performance and numerical correctness of research runs across trainers, rollout workers, and environments with the infrastructure team • Build diagnostics that make unstable runs and performance regressions easier to reproduce and help distinguish algorithm problems from implementation or runtime issues

🎯 Requirements

• Demonstrated depth in at least one of RL algorithms, empirical research on model training, or distributed ML systems • Strong programming skills in Python and experience with PyTorch or a comparable ML framework • Ability to design controlled experiments, interpret results, and debug unexpected model or system behavior • Experience taking a research idea or systems change through implementation, testing, and evaluation, with a clear account of what worked and what didn’t • Experience with distributed training frameworks or asynchronous rollout pipelines • Experience diagnosing unstable RL runs, including policy-staleness or numerical issues • Work on coding agents, verifiable rewards, or long-horizon environments

🏖️ Benefits

• Equity incentives • Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco • Visa sponsorship and relocation assistance for international candidates • Quarterly team off-sites • Hackathons • Conferences and learning opportunities • Opportunity to work with a talented, hard-working and mission-driven team