Top AI & Machine Learning Jobs

Conduct hardware-aware AI research: design and run ML experiments, implement performant, scalable implementations (low-level optimizations), collaborate with silicon and software teams, and publish/present results at conferences.

Develop and optimize high-performance AI compute libraries and kernels (GEMM, convolutions, reductions, fused ops) in C++. Ensure numerical correctness, add microbenchmarks and regression tests, profile and optimize for hardware, debug and improve product quality, work Agile, and mentor teammates.

Build production-grade Go services and Kubernetes components (CRDs/operators) to expose accelerator hardware for scalable AI compute. Ensure reliability, performance, and observability; collaborate with hardware, cloud, and ML teams; lead technical initiatives and mentor engineers.

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.

Lead Go-based development to integrate a new AI accelerator into Kubernetes clusters, mentor engineers, collaborate cross-functionally, troubleshoot complex issues, contribute to upstream projects, and maintain documentation.

Define and drive the architectural vision of the software stack for the Graphcore ML accelerator, ensuring effective communication and maintenance of software architecture across teams.

Lead cross-functional programs for AI networking infrastructure, ensuring alignment between network architecture, hardware development, and operational readiness. Manage supplier relationships and oversee the technical roadmap for scalable AI infrastructure delivery.

Develop, optimize, benchmark and scale state-of-the-art ML models for Graphcore hardware; run large-scale distributed training (64+ accelerators); test internal software releases, identify performance bottlenecks, design and execute ML experiments, collaborate with Research, Software and Product teams, and engage with the AI community.

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.

Develop, optimize, benchmark, and scale large AI models for Graphcore hardware; run distributed training across thousands of accelerators, design experiments, collaborate with research and software teams, and contribute performance improvements and code reviews.