2 of 2 Cloud Spanner Jobs in London

ML & Cloud Infrastructure Engineer - Pioneering AI in 3D London (4-5 Days per week in Office) Up to £180,000 + Equity I'm working with a cutting-edge startup that's building a frontier 3D foundation model at the intersection of AI, computer vision, and spatial computing. Their mission is bold: to transform industries like robotics, AR …/VR, gaming, and film by redefining how 3D content is generated and experienced. They're now looking for a ML & Cloud Infrastructure Engineer who can design, build, and maintain the backbone that powers these ambitious workloads. This is an opportunity to be part of a deep-tech team at the forefront of the AI revolution in 3D … . Why this role? Impact: You'll create the infrastructure that makes advanced ML and 3D workloads possible at scale. Ownership: Full responsibility for cloud deployment, orchestration, and distributed ML pipelines. Innovation: Join a team of bold thinkers working on first-of-its-kind technology. Growth: Be part of a fast-moving startup where your work directly drives More ❯

Role: ML & Cloud Infrastructure Engineer Salary: £140,000 - £160,000 + Equity Location: London - Hybrid (3-5 days a week) Our client is pioneering a frontier 3D foundation model at the intersection of AI, computer vision, and spatial computing. Their mission is to transform how industries from robotics and AR/VR to gaming and film create and … interact with 3D content. They are seeking an ML & Cloud Infrastructure Engineer to design and maintain the scalable infrastructure that powers cutting-edge machine learning workloads and production systems. This is a chance to work at the forefront of AI innovation in 3D technology, with huge scope to shape the platform and practices in a fast-moving startup … environment. The role will involve: Building and maintaining scalable cloud infrastructure (AWS, GCP, Azure) for ML workloads and APIs Setting up ML nodes for distributed training and local development Managing containerised environments (Docker, Kubernetes, Terraform) Optimising storage for big data pipelines supporting ML workloads Monitoring systems and responding to incidents, ensuring reliability and performance Working closely with ML More ❯