Responsibilities
- Build and maintain end-to-end ML infrastructure, including automated training pipelines, model orchestration, and deployment tooling.
- Define and evolve model-serving standards and tooling for low-latency, highly available ML services.
- Develop self-service MLOps capabilities such as feature stores, model registries, and automated performance and data-drift monitoring.
- Enable Kubernetes autoscaling and GPU provisioning and operate a Kubernetes-based development cluster.
- Collaborate with Operations and ML teams to move models from experimentation into GPU-backed production.
- Design observability, define service-level objectives, participate in incident response, and automate repetitive operational work.
- Create standardized workflows and golden paths that help Data Scientists develop and deploy models efficiently.
- Modernize production infrastructure while managing reliability, risk, cost, availability, and continuity.
Requirements
- Experience building and operating machine learning platforms in production environments.
- Working knowledge of Docker, Kubernetes, Linux internals, and model serving at scale.
- Familiarity with ML lifecycle tooling, orchestration frameworks, feature stores, model registries, and drift or performance monitoring.
- Experience owning production systems, including SLOs, observability, incident response, and systematic diagnosis of large-scale failures.
- Production-quality programming experience in Python or a comparable language.
- Experience modernizing production infrastructure with attention to reliability, risk, cost, availability, and continuity.
- Ability to own technical outcomes, use data to support decisions, and communicate clearly with technical and non-technical audiences.
Benefits
- Hybrid work requiring commuting to the Berlin office three times per week.
- Work from almost anywhere for up to 20 days per year.
- Company-paid therapy sessions through SpringHealth and a company-paid HeadSpace subscription.
- Company-wide week off, no-meeting Fridays, paid parental leave, and paid volunteer time.
- Development Dollars, leadership development, and access to thousands of on-demand e-learnings.
- Travel discounts, Employee Resource Groups, six weeks of paid vacation, and a birthday day off.
- Free lunch two days per week, pension plan contributions, public transportation subsidies, and a bike leasing program.
- Monthly social events, Thursday happy hours, sports teams, and an office in Friedrichshain, Berlin.
Tech Stack
Categories
About KAYAK
At KAYAK, we help millions of travelers around the globe make confident travel decisions. We are a leading travel search engine. KAYAK searches other sites to show travelers the information they need to find the right flights, hotels, rental cars and vacation packages. And because we aim to help travelers explore the world more confidently, we’re always on the lookout for ways to make travel planning and trip management easier – offering up a variety of tools and features such as KAYAK Trips, Explore, Price Forecast and our constantly evolving app and A.I. innovations. KAYAK is part of a global network that includes our sister brand OpenTable, as well as a portfolio of travel metasearch brands including Swoodoo, checkfelix, momondo, Cheapflights, Mundi and HotelsCombined. Collectively, we’re uniquely equipped to help people experience the world through travel and dining. KAYAK is part of Booking Holdings Inc. and operates sites in more than 70 countries and territories. KAYAK will never request payment(s) from its candidates for open positions. If you have ever been contacted about a job at KAYAK by phone/Whatsapp or email, and have been asked to provide a Credit Card, wire transfer or crypto payment, please be aware that KAYAK is not associated with this outreach. If you receive a suspicious request regarding a career opportunity at KAYAK, please report it to us. To find out more, visit KAYAK.com.