Senior AI Researcher
Role details
Job location
Tech stack
Job description
As a (senior) AI Researcher for reinforcement learning you will shape and improve the underlying RL methodology, maintain a high-quality training code-base, and conduct large-scale experiments to hill-climb our performance benchmarks. This role is for you if you both have a strong theoretical background on RL and the engineering drive to bring these methods into production and improve on the methods as part of the reinforcement learning team.
In your day-to-day you will conduct large-scale reinforcement learning experiments, derive hypotheses from the results, and iterate on both the implementation and methodology based on the observations. Together with a collaborative team, you will have direct impact on the models that we ship to our customers., * Hill-climb in large-scale training: Conduct large-scale LLM training runs, analyze evaluation scores in depth, propose hypotheses for improvement and directly implement them in order to maximize performance on our benchmarks.
- Theoretical innovation: Stay at the bleeding edge of RL research. You will identify, implement, and iterate on novel approaches to multi-turn reinforcement learning.
- Scale our training infrastructure: Identify bottlenecks in our training setup and optimize our RL training loops for large-scale training.
- Cross-functional collaboration: Partner with our other post-training teams to turn raw feedback into actionable training signals, ensuring that our RL iterations lead to measurable improvements in downstream performance.
Requirements
Do you have a Master's degree?, * A deep understanding of Reinforcement Learning theory and how it relates to modern RL methods.
- Experience with multi-node LLM training (ideally using RL). You understand how to scale multi-node RL trainings and can reason about and implement distributed algorithms.
- Familiarity with statistical methods for evaluation and experiment design.
- Ability to reason about what an evaluation/environment measures and whether it matters - not just run benchmarks, but understand them.
- Strong Python skills and comfort with ML tooling (especially torch distributed)
- Willingness to relocate to Heidelberg or travel regularly (potentially weekly)., * PhD in reinforcement learning or equivalent research experience.
- A history of contributions to top-tier venues (NeurIPS, ICML, ICLR, etc.) specifically regarding RL.
- Experience evaluating LLM models and crafting environments for training.