Senior AI Researcher- Reinforcement learning (f/m/d)

Aleph Alpha is one of the few companies in Europe with end-to-end in-house model development including pre- and post-training. We’re building models that have general-purpose capabilities, but also specifically excel at addressing the needs of our customers.

We're growing our post-training team in Heidelberg (or hybrid in Germany) and are looking for an AI Researcher who combines a deep theoretical understanding of reinforcement learning methods with a desire to improve on the state of the art and improve model capabilities in large-scale training.

At Aleph Alpha, we foster a culture built on ownership, autonomy, and empowerment. Teams and individual contributors are trusted to take responsibility for their work and drive meaningful impact. We maintain a flat organizational structure with efficient, supportive management that enables quick decision‑making, open communication, and a strong sense of shared purpose.

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.

This role is for Aleph Alpha Research GmbH.

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• 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.