Top Tech Jobs & Startup Jobs

Provide IT support for Windows, macOS, and Linux systems, collaborating with engineering teams, troubleshooting issues, and managing user accounts.

Lead QA for Graphcore's telemetry and observability platform. Design and implement end-to-end test strategies, automated frameworks, and realistic large-scale test environments. Validate telemetry pipelines, APIs, performance, scalability, resilience, and integrate tests into CI/CD. Drive root-cause analysis, quality metrics, and release readiness.

Define and integrate SoC subsystems for Graphcore AI acceleration platforms. Turn product requirements into architecture choices across processor, SoC and system levels. Specify interfaces, interconnects and subsystems, produce technical specifications, model and validate designs, and coordinate across design, verification, hardware and software teams to deliver multiple AI silicon projects.

Own and prioritise a component-level backlog, translate feature intent into actionable work, support sprint and PI planning, coordinate cross-team dependencies, define acceptance criteria, and enable the team to deliver technical outcomes aligned to product goals.

Design and run ML experiments, implement and optimize algorithms for hardware efficiency, collaborate with researchers and engineers, and publish and present results at AI conferences.

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 hands-on system validation and debug of AI silicon platforms in the lab. Diagnose PCIe, Ethernet, and SerDes issues, use test equipment (oscilloscopes, BERT, VNA/TDR, protocol analyzers), perform root-cause analysis across signal integrity and subsystems, and drive cross-team technical ownership to bring new platforms to production.