About
At Booking.com, data drives decisions, technology is at the core, and innovation is everywhere.
Responsibilities
•Architect, build, and operate scalable, low-latency distributed ML inference services and prediction systems across online, async, and batch workloads.
•Manage production model deployments, model versioning, feature access, shadow/canary testing, and rollbacks.
•Optimize compute utilization (CPU, GPU, memory) and lead capacity planning to minimize operational costs.
•Operate multi-cloud and on-premise Kubernetes workloads with automated scaling, CI/CD, and IaC practices.
•Implement end-to-end telemetry (metrics, logs, traces, alerts), maintain SLIs/SLOs, and participate in on-call response to drive service resilience.
•Partner with ML Scientists and Engineers to productionize models, improve developer experience, and mentor junior team members.
Requirements
•Experience deploying and operating ML models in production across backend, infrastructure, or SRE domains.
•Proficiency in Java, Scala, JVM languages, or Python with strong backend engineering skills.
•Proven background designing distributed systems (microservices, RPC, queues, streaming, caching, batch).
•Solid grasp of concurrency, resource management, and server-side profiling.
•Hands-on experience managing containerized Kubernetes workloads in production.
•Experience with cloud/hybrid infrastructure, core platform capabilities, IaC, and CI/CD.
•Observability experience using SLIs, SLOs, metrics, logs, dashboards, and alerts.
•Understanding of ML serving (real-time/batch inference, versioning, feature retrieval, monitoring).
•Familiarity with on-call, incident response, postmortems, and runbooks.
•Pragmatic, customer-focused mindset for designing reusable platform capabilities and communicating technical trade-offs.
•Strong cross-team collaboration and English communication skills.
Nice to have
•Experience with ML serving platforms, runtimes (Triton, ONNX Runtime, TF Serving, TorchServe), or gateways.
•Background in JVM tuning, low-latency systems, GPU scheduling, or SRE/platform engineering.
•Familiarity with multi-cloud/hybrid scale, Spark/Kafka/Flink, Terraform, drift monitoring, or GenAI/LLMs.
Conditions
•Annual paid time off and generous paid leave scheme including: parent, grandparent, bereavement, and care leave.
•Hybrid working including flexible working arrangements, and up to 20 days per year working from abroad (home country).
•Industry leading product discounts — up to 1400 per year — for yourself, including automatic Genius Level 3 status and Booking.com wallet credit.
•This role does not come with relocation assistance.
