Design and build systems that enable high-throughput, low-latency inference across CPU and GPU workloads, influence architectural direction, and collaborate closely across platform, product, infrastructure, and research teams to deliver a world-class serving platform.
Compensation
$192,000—$260,000 USD
Responsibilities
Design and implement core systems and APIs that power Databricks Model Serving, ensuring scalability, reliability, and operational excellence.
Partner with product and engineering leadership to define the technical roadmap and long-term architecture for serving workloads.
Drive architectural decisions and trade-offs to optimize performance, throughput, autoscaling, and operational efficiency for CPU and GPU serving workloads.
Contribute directly to key components across the serving infrastructure — from model container builds and deployment workflows to runtime systems like routing, caching, observability, and intelligent autoscaling — ensuring smooth and efficient operations at scale.
Collaborate cross-functionally with product, platform, and research teams to translate customer needs into reliable and performant systems.
Lead Customer Engineer responsible for AI infrastructure and modernization across the UK and Ireland, engaging with technical stakeholders and driving AI workloads on Google Cloud.
Locations (2)
London, England, United Kingdom
Manchester, England, United Kingdom
Responsibilities
Own the technical side of AI infrastructure opportunities in UKI
Help customers choose between TPUs and GPUs on GKE and Compute Engine
Guide customers through assessments of existing environments and produce a prioritised roadmap for modernisation and migration
Work directly with product management and engineering to remove customer blockers
Build and run demos, workshops and proofs-of-concept using agentic tooling
Requirements
Bachelor's degree or equivalent practical experience
Nice to have: designing, sizing and operating training or inference environments at scale
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