Senior MLOps Engineer — Build Scalable ML Platform

Kayak; United States; On-site; USD 125,541 - 159,780; Full time

Benefits offered by this job

Work from (almost) anywhere for up to

Mental health & well-being programs

Company-paid therapy sessions

HeadSpace subscription

Paid parental leave

Paid volunteer time

Leadership development

Development Dollars

Travel Discounts

Employee Resource Groups

6 weeks paid vacation + birthday

Job summary

KAYAK, part of Booking Holdings, is seeking a Senior MLOps Engineer to build and maintain scalable ML infrastructure and production pipelines. You will bridge data science and production engineering within the Machine Learning Platform team.

This role requires commuting to the Berlin office 3 times a week and collaborating with Data Scientists, ML Engineering and Operations teams to deploy robust, production-ready services at scale.

Qualifications

• Experience building and operating ML platforms in production environments.
• Strong knowledge of containerization and orchestration (Docker, Kubernetes).
• Familiarity with ML lifecycle tooling: orchestration frameworks, feature stores, model registries, drift monitoring.
• Experience owning production systems: defining SLOs, building observability, incident response, diagnosing large-scale failures.
• Proficient in Python or similar languages.
• Ability to communicate clearly to technical and non-technical audiences.

Responsibilities

• Build and maintain ML infrastructure end-to-end.
• Own model deployment and serving.
• Develop core MLOps capabilities.
• Operationalize infrastructure with Kubernetes autoscaling and GPU provisioning.
• Improve platform reliability and performance.
• Empower Data Scientists through standardized workflows.

Skills

ML platforms

Python

Observability

Ownership

Tools

Docker

Kubernetes

Prometheus

Grafana

Datadog

Job description

KAYAK, part of Booking Holdings, is seeking a Senior MLOps Engineer to build and maintain scalable ML infrastructure and production pipelines. You will bridge data science and production engineering within the Machine Learning Platform team.

This role requires commuting to the Berlin office 3 times a week and collaborating with Data Scientists, ML Engineering and Operations teams to deploy robust, production-ready services at scale.