Job description

Welcome to Nunatak and our Data & AI Lab! We are looking for a ( Senior)  Machine Learning Engineer (m/f/d) to join our Munich or Berlin offices as soon as possible . Do you live for code, data, and algorithms, but also want to see your models generate real impact in practice? Are you tired of pure proof-of-concepts and eager to build robust, scalable AI solutions? With us, you can expect the perfect blend of in-depth machine learning with top clients and the agile development of innovative AI products and services in our Data & AI Lab. We are looking for a tech expert (m/f/d) who can translate complex models into clean software architectures, master state-of-the-art technologies, and actively drive the AI ​​transformation of our clients. 

Tasks

Your tasks with us:

• Pragmatic prototyping: As an integral part of the Data & AI Lab, you will develop functioning ML prototypes in short sprints to efficiently solve business problems and quickly and tangibly demonstrate the added value for the customer.
• End-to-end responsibility: You take responsibility for the entire process – from clean data preparation and precise modeling to seamless integration into existing interfaces.
• Flexible architectural approaches: In our test environments, you have the freedom to build demos and experiments. However, when it comes to customer systems, you can reliably rely on legally compliant and standards-based architectures.
• Real-World Impact: You test your models for their resilience in real-world operation. For you and us, the actual benefit in the customer process always takes precedence over pure academic perfection.
• Collaboration & Knowledge Transfer: You will work closely  and across disciplines with other ML Engineers, GenAI Application Engineers, and Data & AI Consultants. You will constructively question requirements and ensure a sustainable transfer of know-how to our customers.

Requirements

You bring the following:

• Practical experience: You have 5 years of professional experience in applied machine learning, during which time you have successfully guided at least one model from the initial idea to productive use.
• Tech stack: Solid practical knowledge of common ML frameworks (PyTorch, TensorFlow, scikit-learn) as well as proficient use of the scientific Python stack (e.g. NumPy, Pandas).
• Cloud & Deployment: Proven experience in training, validating and deploying models in at least one established cloud environment (e.g., Google Cloud / Vertex AI, AWS SageMaker or Azure ML).
• Data Engineering Skills: Proficient in SQL and data pipeline architecture. You are experienced in preparing and ensuring the quality of large, complex process and customer data.
• MLOps Mindset: You possess a solid understanding of real-world operations, including model versioning, monitoring, and the continuous refinement of models.
• Academic foundation:  A completed degree in  Computer Science, Data Science, Machine Learning, Informatics, Mathematics, Physics or a comparable course of study with a technical focus.
• Languages: You speak fluent German and English.