Senior Machine Learning Ops Engineer

Who you are

• 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 (for example, using tools such as Prometheus, Grafana, or Datadog), participating in incident response, and diagnosing large-scale failures systematically. You look for opportunities to automate repetitive work rather than absorb it
• Comfort writing production-quality code in Python or a comparable language
• Experience modernizing production infrastructure with attention to reliability, risk, and cost — including thoughtful sequencing of work to maintain availability and continuity for live systems
• The ability to take ownership of technical outcomes, advocate for decisions using data, and communicate clearly in writing and in person — to both technical and non-technical audiences

What the job involves

• Every machine learning model KAYAK ships depends on reliable, scalable infrastructure to move from experiment to production — and that's exactly what this role makes possible
• KAYAK is seeking a Senior MLOps Engineer who will focus on the design and implementation of our machine learning infrastructure and production lifecycle
• This is a senior, hands-on role where you will bridge the gap between data science and production engineering
• You will join the Machine Learning Platform team and be responsible for building and maintaining scalable infrastructure & automated pipelines for model training, deployment, and monitoring, ensuring our ML models are reliable, reproducible, and performant
• You will work closely with Data Scientists, ML Engineering and Operations teams to transform experimental code into robust, production-ready services at scale
• Build and maintain ML infrastructure end-to-end: Extend and operate the infrastructure that powers every model we ship — including CI/CD pipelines, model orchestration, and automated training pipelines designed to scale reliably without manual intervention
• Own model deployment and serving: Help define and evolve the standards and tooling for model serving, ensuring low latency and high availability across our ML services
• Develop core MLOps capabilities: Establish and maintain essential infrastructure that functions as reliable, self-service systems for the entire machine learning organization — with a focus on feature stores, model registries, and automated monitoring for performance and data drift
• Operationalize infrastructure for the ML team: Collaborate with Operations to enable Kubernetes (k8s) autoscaling and GPU provisioning, turning these into accessible, self-service tools for ML practitioners — including standing up and operating a Kubernetes-based development cluster and taking models from experimentation to GPU-backed production
• Improve platform reliability and performance: Partner with Operations to design resilient monitoring using advanced observability tooling
• Define service-level objectives and implement automation to reduce manual interventions and improve system reliability
• Empower Data Scientists through standardized, optimized workflows: Amplify the impact of the ML team by building clear, well-supported "golden paths" — standardized workflows that streamline the model development lifecycle and let Data Scientists focus on modeling while you handle the infrastructure

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