Machine Learning Engineer Manager

Join KAYAK as a Machine Learning Engineering Manager, where you'll lead the development of our shared Machine Learning Platform. This senior, hands-on technical leadership role is based in Berlin and involves owning the technical direction, roadmap, and delivery of the platform. You'll mentor and grow a high-performing team, drive technical direction across platform architecture and the ML lifecycle, and collaborate with various teams to deliver complex initiatives. Enjoy flexible work options, team-wide time off, and opportunities for career growth.

Lead the development and evolution of KAYAK's shared Machine Learning Platform, owning its technical direction, roadmap, and delivery.

About KAYAK

At least 5 years of experience with ML lifecycle systems, including training orchestration, model serving, feature management, and monitoring, with a solid understanding of scalability, reliability, and operational trade-offs

Strong ownership, judgment, and systems thinking, with the ability to balance platform evolution, developer experience, reliability, cost, and technical debt while influencing senior stakeholders

Responsibilities

• Build self-service capabilities for training, deployment, serving, and monitoring, enabling KAYAK teams to ship models safely and at scale.
• Mentor and grow a high-performing team of Machine Learning Platform Engineers, fostering technical excellence, autonomy, and operational accountability.

Requirements

• Curiosity, humility, and a collaborative mindset, with a commitment to continuous improvement and the ability to inspire the same in others
• 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
• Ability to lead across organizational boundaries, aligning teams around shared infrastructure, navigating competing priorities, and driving platform adoption through collaboration rather than authority
• Meaningful people leadership experience, including hiring, coaching, performance management, and developing experienced technical talent, while maintaining a high technical bar and contributing hands-on where it matters most
• Fluency with AI-assisted workflows, leveraging modern tools to improve productivity and quality while critically evaluating their outputs and limitations
• Hands-on experience with technologies relevant to KAYAK’s stack, such as Python, Kubernetes, GitHub Actions, Kafka, AWS, Airflow, and Trino
• Experience building LLM or agent workflows, including prompt or dataset-processing pipelines