[Hiring] Research Scientist / Engineer, Alignment & Reward Modeling @ellamind

Role Description

Picture an agent working through cases at a public authority. It's fast, and it's mostly right. Every so often it also invents a file number, skips a check it was supposed to run, or quietly bends its instructions to finish sooner. For the people we build for, that agent is worse than no agent at all. They're right about that.

So much of our work comes down to one unglamorous question: how do you teach a model to be reliable, and how would you know if you hadn't? That's this job. You'd build the reward models and judges that decide what "good" means during training. You'd train models to be honest about what they did and didn't do. And you'd build the evaluations that catch them when they aren't.

You'd be inside our RL training, not on a separate team reviewing it afterwards. What you build goes straight into the reward.

What you'll work on

• Reward models and LLM judges for work a unit test can't grade: rubric-based, generative, process-level.
• Checking them against careful human judgment, because a judge nobody has checked is just another model with opinions.
• Turning the behaviors that matter in regulated work into training signal.
• Following policy and instructions when it's inconvenient.
• Asking when something is ambiguous.
• Stopping instead of guessing.
• Reporting what a tool actually returned.
• Knowing how sure to be.
• Hunting reward hacking and specification gaming, and studying how our judges fail so we can fix them.
• Alignment experiments inside RL post-training (preference data, synthetic critiques, principle-based feedback), with an honest account of what they cost in capability and efficiency.
• Safety and reliability evals that still mean something outside the lab.

Qualifications

• You've trained or evaluated LLMs, and got your hands dirty in at least one of these: reward modeling, RLHF or preference learning, LLM-as-judge, alignment or safety evaluation.
• Your Python and PyTorch are strong enough to take an experiment from idea to result on your own.
• You're suspicious of metrics. When a number looks good, your first thought is how it could be wrong.
• You can take a fuzzy complaint ("it's overconfident on edge cases") and turn it into something we can measure and train against.
• You write clearly. An alignment result nobody believes hasn't achieved much.
• You live in the EU and are allowed to work here. English is all you need; German is a bonus.

Nice to have

• Interpretability or scalable-oversight work.
• Publications on reward modeling or evaluation.
• Time spent in a regulated field.
• Some familiarity with the EU AI Act.
• German language skills.

Benefits

• Work from anywhere in the EU, ideally Germany.
• Real compute: hundreds of GPUs across EuroHPC and commercial compute.
• Whatever AI tools make your work easier, without a procurement saga.
• Your name on public work. Our first open-source release is planned for Q1 2027.
• Founders you can just message, and a research team worth learning from.
• An office if you want one: Bremen, right on the water (yes, there's a boat, and free drinks and smoothies), or Berlin.

How to apply

Upload your CV through the form and tell us, in a few lines, about one thing you built that you're proud of. An environment, a training run, a judge, a bug that took you three days to corner. Links to GitHub, papers, a blog post or a messy notebook are all welcome. Please don't write us a polished cover letter. We'd rather read the notebook.

We're hiring for three RL roles at the same time: environments and data, post-training, and alignment and reward modeling. If you're somewhere between two of them, apply to the closer one and say so. We'll figure it out together.

We care a lot more about what you've built and how you think than about how your CV reads. If you don't tick every box, apply anyway. Hardly anyone does, and different perspectives make our models and our team better.

Company Description

At ellamind, we develop language models and the methods that make them better. Our team trains its own models, builds RL environments, datasets and benchmarks, and researches how to reliably measure and systematically improve the capabilities of LLMs. We are known for our leading German-language open-source models.

As a cash-flow-positive startup, we work with well-known partners such as Deutsche Telekom, the German Federal Government and leading healthcare providers. Our headquarters is in Bremen. We offer competitive pay, equity options and a short line to the founders.

Your CV isn't the only thing that counts. We are looking for curious people who want to help shape the next generation of European AI models. Diversity matters to us, because different perspectives make us better. Please apply even if you don't (yet) meet every requirement.