Design and build systems that enable high-throughput, low-latency inference across CPU and GPU workloads for Databricks Model Serving.
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
Nice to have: capacity planning
Principal Product Manager, AI Infrastructure and Orchestration
Lead the design, API, and operational aspects of the AI infrastructure control plane, focusing on workload placement, scaling, and governance across Kubernetes clusters and accelerators.
Responsibilities
Own the deployment and workload API, including resource model and lifecycle semantics
Define placement, capacity, and autoscaling policies for GPU and accelerator workloads
Design and enforce multi‑tenant isolation, quota, and fairness mechanisms
Develop and maintain traffic routing, ingress, and private connectivity for model and agent endpoints
Ensure governance, audit, and metering for customer usage
Lead backend and infrastructure engineering for Salesforce’s internal Kafka-as-a-Service platform, designing, developing, and operating distributed systems across global cloud environments.
Compensation
For British Columbia-based roles, the base salary hiring range for this position is CAD 130,300 to CAD 179,200.
Responsibilities
Design and develop resilient distributed systems that run across thousands of compute nodes in multiple data centers
Deliver cloud infrastructure automation tools, frameworks, workflows, and validation platforms on public cloud platforms such as AWS, GCP, Azure, or Alibaba
Write microservices on containerization frameworks such as Kubernetes, Docker, Mesos
Develop Infrastructure-as-Code using Terraform
Participate in on-call rotation to address complex problems in real-time and keep services operational and highly available
Senior DevOps Engineer to own and evolve AWS cloud infrastructure, drive FinOps discipline, and ensure systems are scalable, secure, and cost‑efficient.
Responsibilities
Own and evolve AWS cloud infrastructure across multi‑account environments
Architect solutions and automate operations
Implement and manage Infrastructure as Code using Terraform
Ensure infrastructure security through IAM, VPC design, encryption, and compliance controls
Seeking a Senior Backend Engineer to work on NG-SIEM systems, focusing on large-scale distributed systems, intelligent placement, auto balancing, and autoscaling within a multi-tenant environment.
Locations (4)
Sunnyvale, California, United States
Redmond, Washington, United States
New York City, NY, United States
Austin, Texas, United States
Compensation
base salary range for this position for all U.S. candidates is $140,000 - $220,000 per year, with eligibility for bonuses, equity grants
Responsibilities
Intelligent Customer Placement: Decide which part of the fleet each customer lands on, bin packing efficiently
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