Top AI & Machine Learning Jobs in London

Validate and benchmark ML models on Graphcore's software/hardware stack. Create automated benchmarking pipelines, run open-source models, design targeted tests for numerical precision, quantisation, attention, distributed execution, and debug performance and correctness issues across frameworks and environments.

Design and build test infrastructure, automation, and tooling to validate ML software stacks and hardware; shape test architecture across CI pipelines, frameworks, simulators, emulators, and large ML workloads; mentor engineers and own complex validation projects.

Validate and benchmark ML models and stacks on Graphcore hardware. Build automated benchmarking pipelines, run open-source models, create targeted tests for numerical precision, quantisation, distributed execution and subgraphs, and debug performance and correctness across frameworks and execution environments.

Build and maintain infrastructure, tools and services to support AI research and engineering. Deploy and operate services with Kubernetes and Docker, manage cloud infrastructure (Terraform/AWS), support CI/CD, and enable ML application deployment, orchestration, and accelerator hardware management.

Conduct research in visual generative modelling, multimodal learning and world models; design and run experiments, publish findings, and collaborate with engineering and silicon teams to align models with hardware-aware ML and AI compute.

Conduct hardware-aware AI research: generate ideas, design and run ML experiments, implement performant scalable systems, publish and present results, and collaborate with researchers, silicon and software engineers to guide next-generation AI hardware.

Develop, implement and optimise large-scale ML models for Graphcore hardware; benchmark and scale training/inference across thousands of accelerators; test internal releases, fix and review code; design and run experiments; collaborate with Research, Software and Product teams; engage with the AI community.