Aleph Alpha is one of the few companies in Europe doing serious foundation model pre-training like our recently released Kolibri-1 model. Our customers - in finance, manufacturing, public administration - need models that understand German, meet European regulatory requirements, and work reliably in high-stakes settings. We're building that in Heidelberg.
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.
We believe a strong engineering culture is the key to model training success. We like Extreme Programming and favor trunk-based development. We often mob-program, which keeps us aligned and means we always learn from each other.
As an AI R&D Engineer in Model Training (f/m/d), you will join our model training team in Heidelberg or Berlin. Most of the team is based in Heidelberg. The model training team is building and improving our model training pipeline, which has been used to train Kolibri.
You will work across our full stack. Some weeks you might be optimizing how training loads are scheduled on our cluster and making the pipeline more robust and performant so we can iterate faster. Other weeks, you will be enabling large-scale code execution for reinforcement learning. And at other times, you might dig deep into our evaluation codebase to lift inference throughput on evals.
No two days are the same. Things move fast, and your ability to focus and prioritize is what lets you unblock the team day-to-day while designing the high-quality tooling and infrastructure that speeds us up long-term.
We're still building out our training pipeline and infrastructure. Some pieces exist, some do not, and you will have real influence on what gets built and how. Your work directly shapes how quickly we can experiment and improve our models.
Identifying issues and taking initiative is what drives our day-to-day work.
Working mode, design decisions, priorities are all done in your team together with your team mates.
The strongest teams often use highly collaborative techniques, like mob or pair programming.
Due to the highly collaborative nature of our work you are expected to be on-site at least two days a week. If you are in Berlin, you should expect to travel around every second month for a few days to Heidelberg.
We are specifically looking to strengthen the Software Engineering in the teams which are research heavy. Major points include improving the maintainability of our code through tests and design & architecture changes.
At its core, you need to be able to dive into a codebase, change the behavior as necessary, and after the change the code should be better tested and easier to understand than before.
As such we look for candidates for an understanding of how to design for low coupling and high cohesion paired with the ability to incrementally refactor.
Experience with the following technologies is likely to be helpful:
Test Driven Development (TDD)
Trunk based development
Continuous integration
Domain Driven Design
Most of our code is in Python, there is also the occasional Rust codebase.
Large parts of our training pipeline are built on top of Kubernetes.
We do not hire for skills in a particular technology or language, but rather ability to learn and mindset, yet experience with these technologies is a plus.
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
JobRad® Bike Lease
Note: We welcome applicants at every stage of their career. Your drive and willingness to learn are far more important to us than strict experience requirements.