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Staff Engineer (ML Engineer)

Posted 11 days ago

OfficeCambridge, UKSE

About the job

Validate the ML stack that turns accelerator hardware into trusted AI performance.

This role sits where modern ML models meet Graphcore’s software and hardware stack. You will test, benchmark and validate complex systems before they reach customers.

Your work will expose regressions, correctness issues and performance limits across frameworks, models and execution environments. You will help teams understand what is working, what is breaking and why.

You will run open source models, build automated benchmarking pipelines and create targeted tests for low level ML behaviour. That includes numerical precision, quantisation, attention mechanisms, distributed execution and model subgraphs.

This is a role for someone who wants to stay close to how AI systems really work. You will not design new models, but you will make them run reliably on ambitious infrastructure.

The team and culture

The ML QA team is where Graphcore’s ML software stack comes together for validation. Work spans unit tests, full model benchmarks, distributed workloads, simulators, emulators and hardware targets.

Engineers are expected to take ownership, question assumptions and improve how testing is done. The team moves quickly, but decisions are grounded in evidence, benchmark data and technical discussion.

You will work closely with software, infrastructure and hardware teams. Squads organise around priorities, with space for engineers to shape roadmaps and raise the quality bar.

What we’re looking for

· Strong experience in Machine Learning or ML-adjacent software engineering roles.
· A solid grasp of neural networks, training, inference, numerical precision and performance trade-offs.
· Hands-on experience with PyTorch, TensorFlow, JAX, Triton or similar ML frameworks and tools.
· Strong Python skills for automation, experimentation, benchmarking and reporting.
· Experience designing, running and analysing ML benchmarks or model experiments.
· Confident debugging skills in Linux, with curiosity about model behaviour and system performance.

Benefits

· Unlimited annual leave
· Up to 5% matched pension
· Phantom equity – share in Graphcore’s success
· True flexibility in how and where you work
· Office spaces designed for collaboration
· Free food and an on-site barista
· Health cash plan
· Income protection
· Life assurance
· Along with other benefits you can choose from (private medical insurance, dental plan etc)

We welcome people from all backgrounds and experiences and are committed to building an inclusive environment where everyone can do their best work.

We’re an equal opportunity employer and recognise that everyone brings different strengths and perspectives. If you need any adjustments during the interview process, just let us know - we’re happy to support you.

Join the Team at Graphcore

Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.

As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.

Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore brings together deep expertise to solve complex problems and deliver meaningful progress in AI compute.

If you want to validate the systems behind next generation AI compute, we’d love to hear from you. Apply now to help build what comes next.

Job details
Workplace
Office
Location
Cambridge, UK
Experience
SE

Graphcore has built a new type of processor for machine intelligence to accelerate machine learning and AI applications for a world of intelligent machines

Key team members

Mark Arnold

Mark Arnold

Shannon Morton

Shannon Morton

Mike Wangsmo, PE

Mike Wangsmo, PE

Simon Chambers

Simon Chambers

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