Machine Learning Engineer Manager

Who you are

• 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 a 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 where it matters most
• Strong ownership, judgment, and systems thinking, with the ability to balance platform evolution, developer experience, reliability, cost, and technical debt while influencing senior stakeholders
• Ability to lead across organizational boundaries, aligning teams around shared infrastructure, navigating competing priorities, and driving platform adoption through collaboration rather than authority
• Fluency with AI-assisted workflows, leveraging modern tools to improve productivity and quality while critically evaluating their outputs and limitations
• Curiosity, humility, and a collaborative mindset, with a commitment to continuous improvement and the ability to inspire the same in others
• 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

What the job involves

• KAYAK 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
• You’ll lead a Berlin-based team which builds the shared infrastructure and developer workflows that take models from experimentation to reliable production.
• You’ll own its technical direction, roadmap, and delivery while staying hands-on with architecture and the hardest engineering problems.
• In partnership with the team, you’ll build self-service capabilities for training, deployment, serving, and monitoring so KAYAK teams can ship models safely and at scale
• Own the strategy and roadmap for KAYAK’s shared Machine Learning Platform, aligning technical investments with business needs and long-term platform evolution
• Lead, mentor, and grow a high-performing Berlin-based team of Machine Learning Platform Engineers, fostering technical excellence, autonomy, and operational accountability
• Drive technical direction across platform architecture and the ML lifecycle, contributing hands-on to high-impact problems and simplifying legacy systems where it creates clear value
• Build and evolve shared ML capabilities, including training orchestration, model serving, feature management, and LLM workflows, improving developer experience and enabling teams to deliver production-ready ML solutions efficiently
• Champion platform reliability and operational excellence, establishing robust monitoring, alerting, incident response, and production-readiness standards
• Define clear ownership boundaries so the platform team owns shared infrastructure and lifecycle tooling, while downstream teams own use-case-specific model and business logic
• Collaborate with Data Science, Engineering, and Operations teams to plan and deliver complex, multi-team initiatives, ensuring clear alignment on scope, dependencies, and timelines

Benefits

• Work flexibly: Our global flexible work policy lets our teams decide if they head into one of our offices or work from anywhere else
• Team-wide time off: Most of our team disconnects at the same time each year to recharge and reconnect
• Global volunteer week: Take VTO (Volunteer Time Off) to support the organizations and charitable efforts you believe in
• Wellness: We take care of each other - providing opportunities and spaces to live that work-life balance
• Resource groups: Our five ERGs are employee-led groups for underrepresented communities and their allies
• Career growth: We provide opportunities for our team to develop their skills – or learn new ones – on their own terms