Own the end-to-end lifecycle of ML systems, with focus on deployment and monitoring
Refactor and productionize existing models and notebooks into clean, modular, and testable code.
Introduce and enforce best practices in coding, testing, model versioning
Set up and improve CI/CD workflows for ML systems
Work closely with Data Scientists and Data Engineers to ensure clean interfaces and reliable data flows
Act as a technical reference within the team, reviewing code and guiding less experienced members.

3+ years of experience building and deploying ML systems in production environments
Strong software engineering skills in Python, with a focus on writing clean, maintainable, and testable code
Proven experience taking models from notebook to production
Solid understanding of model development lifecycle
(Bonus) Familiarity with containerization (Docker)
(Bonus) Experience with cloud environments (AWS preferred) and data platforms such as Snowflake
Pragmatic and solution-oriented: you focus on what works in production, not just in theory
Comfortable working in ambiguous environments and bringing structure where needed
Quality-driven mindset with attention to maintainability and long-term scalability.

An international team – Work with colleagues from 10+ nationalities.
A dynamic and innovative environment – Be part of a high-performing team.
Hybrid work model – 3 days in-office, 2 days remote.
Impactful work – Contribute to a challenging and innovative project.
Flexible benefits – allocate part of your gross salary (pre-tax) to meal vouchers, transport allowance, and private healthcare at a preferential rate.
Office perks – Fresh fruit and specialty coffee daily.

Machine Learning Engineer

Key skills