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KAYAK, part of Booking Holdings, is seeking a Machine Learning Engineering Manager to lead the development and evolution of its shared Machine Learning Platform. This is a senior, hands-on technical leadership role based in Berlin, requiring 3 days per week in the office.
You will lead a Berlin-based team of Machine Learning Platform Engineers responsible for building shared infrastructure and developer workflows that take models from experimentation to reliable production. You'll own the technical direction, roadmap, and delivery of the ML platform while staying hands-on with architecture and complex engineering problems. Working in partnership with your team, you'll build self-service capabilities for training, deployment, serving, and monitoring so KAYAK teams can ship models safely and at scale.
Key responsibilities include:
- Owning the strategy and roadmap for KAYAK's shared Machine Learning Platform, aligning technical investments with business needs and long-term platform evolution
- Leading, mentoring, and growing a high-performing team of ML Platform Engineers, fostering technical excellence, autonomy, and operational accountability
- Driving technical direction across platform architecture and the ML lifecycle, contributing hands-on to high-impact problems and simplifying legacy systems
- Building and evolving shared ML capabilities including training orchestration, model serving, feature management, and LLM workflows
- Championing platform reliability and operational excellence through robust monitoring, alerting, incident response, and production-readiness standards
- Defining clear ownership boundaries between the platform team and downstream teams
- Collaborating with Data Science, Engineering, and Operations teams on complex, multi-team initiatives
This role combines people leadership with hands-on technical contribution, requiring someone who can balance platform evolution, developer experience, reliability, cost, and technical debt while influencing senior stakeholders.
REQUIREMENTS:
- Strong technical foundations in software engineering, machine learning, and systems design, backed by significant hands-on experience building and operating production ML systems, platforms, or infrastructure
- At least 5 years of experience with ML lifecycle systems, including training orchestration, model serving, feature management, and monitoring, with solid understanding of scalability, reliability, and operational trade-offs
- Meaningful people leadership experience, including hiring, coaching, performance management, and developing experienced technical talent, while maintaining a high technical bar and contributing hands-on
- Strong ownership, judgment, and systems thinking, with ability to balance competing priorities while influencing senior stakeholders
- Ability to lead across organizational boundaries, aligning teams around shared infrastructure and driving platform adoption through collaboration
- Fluency with AI-assisted workflows, leveraging modern tools while critically evaluating their outputs and limitations
- Curiosity, humility, and collaborative mindset with commitment to continuous improvement
BONUS QUALIFICATIONS:
- Hands-on experience with Python, Kubernetes, GitHub Actions, Kafka, AWS, Airflow, and Trino
- Experience building LLM or agent workflows, including prompt or dataset-processing pipelines
