Senior ML Ops Engineer

Booking Holdings Inc.

Job Overview

Location

Berlin Office

Job Type

Full-time

Full Job Description

📋 Description

• Build and maintain end-to-end ML infrastructure that powers production machine learning models, including CI/CD pipelines, model orchestration, and automated training systems designed to scale without manual intervention.
• Own the design and evolution of model deployment and serving standards, ensuring low latency and high availability across all ML services at scale.
• Develop and maintain core MLOps capabilities such as feature stores, model registries, and automated monitoring systems for performance and data drift.
• Operationalize Kubernetes (k8s) autoscaling and GPU provisioning into self-service tools for data scientists, including standing up and operating a Kubernetes-based development cluster to transition models from experimentation to GPU-backed production.
• Partner with Operations teams to design resilient monitoring using advanced observability tooling, define service-level objectives (SLOs), and implement automation to reduce manual interventions and improve system reliability.
• Empower data scientists by creating standardized, optimized "golden paths" for the model development lifecycle, enabling them to focus on modeling while infrastructure is handled autonomously.
• Collaborate closely with Data Scientists, ML Engineering, and Operations teams to transform experimental code into robust, production-ready services that meet performance and reliability requirements.
• Maintain production-quality code in Python or comparable languages to support ML infrastructure automation, tooling, and system integrations.
• Modernize production infrastructure with attention to reliability, risk mitigation, and cost efficiency, sequencing work to ensure continuous availability of live systems.
• Participate in incident response and systematic diagnosis of large-scale failures, prioritizing automation to prevent recurrence.
• Advocate for technical decisions using data and communicate clearly with both technical and non-technical stakeholders in writing and in person.
• Ensure all ML models are reliable, reproducible, and performant through scalable infrastructure and standardized operational practices.
• Work in the Berlin office three times per week to collaborate in person with cross-functional teams.

🎯 Requirements

• Experience building and operating ML platforms in production environments.
• Solid working knowledge of containerization and orchestration (Docker, Kubernetes), Linux internals, and model serving at scale.
• Familiarity with ML lifecycle tooling, including orchestration frameworks, feature stores, model registries, and drift or performance monitoring.
• Experience owning production systems: defining service-level objectives (SLOs), building observability (e.g., Prometheus, Grafana, Datadog), participating in incident response, and diagnosing large-scale failures systematically.
• Comfort writing production-quality code in Python or a comparable language.
• Experience modernizing production infrastructure with attention to reliability, risk, and cost, including sequencing work to maintain availability for live systems.

🏖️ Benefits

• Work from (almost) anywhere for up to 20 days per year.
• Company-paid therapy sessions through SpringHealth and company-paid subscription to Headspace.
• Company-wide week off per year with no work pile-up.
• No meeting Fridays.
• 6 weeks paid vacation plus a day off for your birthday.
• Free lunch two days per week.

Skills & Technologies

Python

Docker

Kubernetes

Linux

Prometheus

Senior

Onsite

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Booking Holdings Inc.

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About Booking Holdings Inc.

Kayak is a travel search engine and booking platform that aggregates fares and rates for flights, hotels, rental cars, and vacation packages from hundreds of travel sites and suppliers. It provides price comparison tools, price alerts, mobile apps, and itinerary management features to help consumers find and organize travel options efficiently. The company operates globally and is headquartered in Stamford, Connecticut.

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