KAYAK, part of Booking Holdings (NASDAQ: BKNG), is the world's leading travel search engine. With billions of queries across our platforms, we help people find their perfect flight, stay, rental car and vacation package. We're also transforming business travel with a new corporate travel solution, KAYAK for Business.

As an employee of KAYAK, you will be part of a travel company that operates a portfolio of global metasearch brands including momondo, Cheapflights and HotelsCombined, among others. From start-up to industry leader, innovation is at our core and every employee has an opportunity to make their mark. Our focus is on building the best travel search engine to make it easier for everyone to experience the world.

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.

If you’re excited to combine people leadership with building the systems that make machine learning work in production, we’d love to hear from you.

This role will be required to work from our Berlin office 3 days per week.

In this role, you will:

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.

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.

Bonus qualifications

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

Benefits and Perks:

Work from (almost) anywhere for up to 20 days per year
Focus on mental health and well-being:
- Company-paid therapy sessions through SpringHealth
- Company-paid subscription to HeadSpace
- Company-wide week off a year - the whole team fully recharges (and returns without a pile-up of work!)
No meeting Fridays
Paid parental leave
Paid volunteer time
Focus on your career growth:
- Development Dollars
- Leadership development
- Access to thousands of on-demand e-learnings
Travel Discounts
Employee Resource Groups
6 weeks paid vacation + a day off for your birthday
Free lunch 2 days per week
Pension plan contributions
Public transportation subsidies
Bike leasing program
Monthly social events, Thursday happy hours, sports teams
An awesome office in Friedrichshain, Berlin

Inclusion

At KAYAK, we want everyone to have the space to grow, share ideas and do great work. That’s why we’re focused on hiring the best talent from all walks of life and experiences, supporting them well and making sure no one feels like they have to fit a mold to belong here.

Need any adjustments for the interview, application or on the job? No problem - just give us a heads-up. We’ve got you.

#J-18808-Ljbffr

Momondo Group Limited