Prime Intellect - Research Engineer / Scientist

• Algorithm research • Algorithm research • 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. • Scaling science • Scaling science • 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, then test whether the improvements hold across larger models and longer runs. • RL frameworks • Extend prime-rl to support new algorithms, models, and asynchronous training workflows. • Improve the performance and numerical correctness of research runs across trainers, rollout workers, and environments, working 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.