Staff Engineer (ML Engineer)

EngineersOfAI; Greater London; On-site; GBP 60,000 - 80,000; Full time

Benefits offered by this job

Unlimited annual leave

Matched pension up to 5%

Phantom equity

Flexible work environment

Free food and on-site barista

Health cash plan

Life assurance

Job summary

EngineersOfAI in Greater London seeks an experienced ML Engineer to validate and benchmark the ML stack that powers AI performance. You'll work with various engineering teams to ensure that complex systems are reliable and efficient before reaching customers.

The role requires strong expertise in Machine Learning and Python, along with a solid understanding of ML frameworks such as PyTorch and TensorFlow. Enjoy unlimited annual leave, matched pension contributions, and more in a flexible work environment.

Qualifications

• 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 ML frameworks and tools.

Responsibilities

• Validate the ML stack and benchmark complex systems.
• Run open source models and build automated benchmarking pipelines.
• Work closely with software, infrastructure, and hardware teams.

Skills

Machine Learning experience

Neural networks knowledge

Python skills for automation

ML frameworks (PyTorch, TensorFlow)

Debugging skills in Linux

Job description

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.