We are a young company with deep experience in AI and scientific data analysis. We build proprietary, patented AI systems for spectral analysis based on magnetic resonance data, and we work with international clients on hard, real-world problems.
We are a small team of highly qualified people. We like smart, practical thinking. We care about human relationships, intellectual honesty, and the freedom to do serious work without pointless bureaucracy. We are trying to build a high-talent-density company where strong people can do the best work of their careers.
If that sounds like your kind of environment, keep reading.
You will help us design, build, deploy, and improve machine learning systems used in production.
In practice, that means you will:
- design and implement machine learning and deep learning models;
-
work on both neural networks and classical ML approaches, depending on what actually works;
-
build data pipelines and production-ready ML services;
-
deploy and monitor ML systems in real environments;
-
collaborate with software engineers, scientists, and clients;
-
support projects across multiple industries, not just one narrow use case;
-
help shape technical decisions on both the product side and the client-delivery side.
This is not a “train a model and throw it over the wall” job. We want someone who likes owning the full path from problem framing to production impact.
Our stack is evolving, but today this role is likely to work with:
- Python;
-
PyTorch and/or TensorFlow;
-
scikit-learn, NumPy, pandas;
-
cloud platforms such as AWS, GCP, or Azure;
-
containers and backend tooling;
-
distributed or scalable data-processing workflows where needed.
If you are strong in the fundamentals and have shipped real systems, we care more about depth and judgment than an exact line-by-line match.
Essential requirements
- At least 3 years of relevant experience in machine learning, applied AI, data science, or closely related software engineering roles.
-
Strong foundations in mathematics, statistics, computer science, or a related STEM field.
-
Proven experience designing and shipping ML systems in production.
-
Strong Python skills.
-
Experience with modern ML frameworks, especially PyTorch or TensorFlow.
-
Experience working with cloud platforms and production environments.
-
Strong problem-solving skills and the ability to work with incomplete information.
-
Good written and spoken English.
-
Comfort working in a startup environment with a lot of ownership and not much hand-holding.
Desirable requirements
- Previous experience in a startup or other high-ownership environment.
-
Experience with MLOps, model monitoring, CI/CD, or distributed computing.
-
Experience with spectral, chemical, physical, or other scientific/industrial data.
-
International study or work experience.
-
Master's degree, PhD, or equivalent depth in a STEM field.
-
Experience working directly with clients, users, or technical stakeholders outside the ML team.
We place offers inside the published salary band using consistent criteria, including:
- how much hands-on experience you have building production ML systems;
-
the complexity of the systems you have actually shipped;
-
your depth in Python, ML engineering, cloud, and deployment;
-
your ability to work independently and make good technical decisions;
-
your fit with our scientific and industrial problem space;
-
your communication skills, especially when working across technical and non-technical teams.
In simple terms: the more you can independently design, ship, and support high-impact ML systems in our context, the higher your offer is likely to be within the band.
We review compensation using the same general principles: scope, impact, technical level, and sustained contribution.
We believe early employees should share in the value they help create.
We have reserved 10% of the company for employee equity, focused especially on early hires. This role is eligible for a grant under the company's employee equity plan. The exact grant depends on seniority, timing, scope, and the cash/equity balance of the final offer.
For this role, the typical grant is 0.15%–0.40% fully diluted equity. Exceptional hires may receive more.
Standard vesting is 4 years with a 1-year cliff.
- €30,000–€35,000 gross annual salary
-
Equity
-
1 extra paid day off for your birthday
-
Hybrid work
-
On-campus kindergarten access, subject to availability and internal policy
-
A humane environment with flexibility, trust, and very little nonsense
