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

Aleph Alpha
Heidelberg

Jetzt bewerben

Details zur Stelle

Vollzeit

Leistungen

Flexible Arbeitszeiten

Qualifikationen

Torch
Node.js
Doktorat
Master
Künstliche Intelligenz
Python

Vollständige Stellenbeschreibung

Our Mission

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.

Team Culture

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.

About the role

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.

Your Responsibilities

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.

Your Profile

Basic Qualifications

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).

Preferred Qualifications

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.

Compensation and Benefits

Become part of an AI revolution!
30 days of paid vacation
Access to a variety of fitness & wellness offerings via Wellhub
Mental health support through nilo.health
Substantially subsidized company pension plan for your future security
Subsidized Germany-wide transportation ticket
Budget for additional technical equipment
Flexible working hours for better work-life balance and hybrid working model
Virtual Stock Option Plan
JobRad Bike Lease

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