Staff Engineer (ML Engineer) in London

Staff Engineer (ML Engineer) in London

London Full-Time 60000 - 80000 € / year (est.) Home office (partial)
graphcore

At a Glance

  • Tasks: Benchmark and validate ML models, ensuring performance and reliability across various environments.
  • Company: Join Graphcore, a leader in AI compute backed by SoftBank, shaping the future of technology.
  • Benefits: Enjoy flexible working, generous leave, private health insurance, and a vibrant office culture.
  • Other info: Inclusive workplace with excellent career growth opportunities and a focus on innovation.
  • Why this job: Make a real impact in AI while collaborating with top minds in a dynamic environment.
  • Qualifications: Experience in ML engineering, strong Python skills, and familiarity with major ML frameworks required.

The predicted salary is between 60000 - 80000 € per year.

About Graphcore

At Graphcore, we’re building the future of AI compute. We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacentre scale. As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem. To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world. We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

Applicants for this role should have strong experience working with machine learning systems and frameworks, along with a solid understanding of core AI concepts and model behaviour. The role centres on testing, validating, and benchmarking a complex ML software stack, with a particular focus on performance, reliability, and correctness across modern AI workloads. The ideal candidate is an experienced ML engineer who understands how contemporary models are trained and executed, and who has hands-on experience debugging functional and performance issues in ML systems. This person will be comfortable working with industry-standard frameworks and state-of-the-art models, bringing them up on internal infrastructure, and collaborating closely with software and hardware teams in a technically demanding environment spanning ML frameworks, infrastructure, and AI accelerator hardware.

The Team

The ML QA team is composed of highly skilled software engineers with a strong focus on automation, software quality, and data-driven validation. The team works closely with industry-standard machine learning frameworks and models, contributing to upstream open-source projects and collaborating across the wider software organization. Operating in a fast-paced environment, the team plays a critical role in ensuring reliability, performance, and maintainability across the ML software stack, helping to deliver robust and high-quality products to customers.

Responsibilities and Duties

  • Benchmark ML models and frameworks, analysing results to identify regressions, performance bottlenecks, and correctness issues.
  • Work hands-on with industry-standard ML frameworks to validate functionality and performance across different execution environments.
  • Build and maintain automated testing and benchmarking pipelines targeting simulators, emulators, and physical hardware.
  • Collaborate closely with software teams to ensure adequate test coverage for new and existing features.
  • Develop tooling and scripts (primarily in Python) to support testing, benchmarking, and functional reporting.
  • Take ownership over aspects of our testing and infrastructure, owning the roadmap and driving innovation independently.

Candidate Profile

Essential:

  • Experience working in Machine Learning or ML-adjacent engineering roles.
  • Strong foundation in core AI and ML concepts (e.g. neural networks, training vs inference, numerical precision, performance trade-offs).
  • Hands-on experience with one or more major ML frameworks such as PyTorch, TensorFlow, JAX, or similar.
  • Strong proficiency in Python for ML workflows, experimentation, and automation.
  • Experience designing, running, and analysing ML benchmarks or experiments.
  • Experience working in Linux environments.
  • Strong analytical and debugging skills, with the ability to reason about model behaviour and system performance.
  • Bachelor/Master's/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field.

Desirable

  • Experience with MLOps pipelines, model deployment, or production ML systems.
  • Familiarity with performance analysis, profiling tools, or numerical accuracy validation.
  • Exposure to distributed training or inference systems.
  • Experience with hardware-accelerated ML, compilers, or system-level performance considerations.
  • Familiarity with CI/CD systems used for ML workflows.
  • Experience contributing to open-source ML frameworks or tooling.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications.

Staff Engineer (ML Engineer) in London employer: graphcore

Graphcore is an exceptional employer, offering a dynamic work environment in the heart of Bristol where innovation thrives. With a strong commitment to employee growth, we provide extensive benefits including flexible working arrangements, generous leave policies, and comprehensive health plans, all while fostering a culture of inclusivity and collaboration among some of the brightest minds in AI. Join us to make a meaningful impact on the future of artificial intelligence and enjoy unique perks like our in-house barista bar and healthy snacks.

graphcore

Contact Detail:

graphcore Recruiting Team

StudySmarter Expert Advice🤫

We think this is how you could land Staff Engineer (ML Engineer) in London

Tip Number 1

Network like a pro! Reach out to current employees at Graphcore on LinkedIn or other platforms. A friendly chat can give you insider info and maybe even a referral, which can really boost your chances.

Tip Number 2

Prepare for the technical interview by brushing up on your ML frameworks and Python skills. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and solve problems!

Tip Number 3

Show your passion for AI and ML! During interviews, share your thoughts on recent advancements in the field or any personal projects you've worked on. This will help us see your enthusiasm and commitment to the industry.

Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Graphcore.

We think you need these skills to ace Staff Engineer (ML Engineer) in London

Machine Learning Systems
AI Concepts
Model Behaviour
Benchmarking ML Models
Performance Analysis
ML Frameworks (e.g. PyTorch, TensorFlow, JAX)
Python Programming

Some tips for your application 🫡

Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience with machine learning systems and frameworks. We want to see how your skills align with the role, so don’t hold back on showcasing your relevant projects!

Show Off Your Technical Skills:When detailing your experience, focus on your hands-on work with ML frameworks like PyTorch or TensorFlow. We love seeing specific examples of how you've tackled performance issues or validated model behaviour in your previous roles.

Be Clear and Concise:Keep your application straightforward and to the point. Use bullet points for your achievements and responsibilities to make it easy for us to see your qualifications at a glance. Remember, clarity is key!

Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy to do!

How to prepare for a job interview at graphcore

Know Your ML Frameworks

Make sure you brush up on your knowledge of major ML frameworks like PyTorch and TensorFlow. Be ready to discuss your hands-on experience with these tools, as well as any specific projects where you've used them to solve real-world problems.

Demonstrate Your Debugging Skills

Prepare to showcase your analytical and debugging skills. Think of examples where you've identified performance bottlenecks or correctness issues in ML systems, and be ready to explain how you approached these challenges.

Understand the AI Compute Stack

Familiarise yourself with the complete AI compute stack, from silicon to software. Being able to discuss how different components interact will show that you have a holistic understanding of the field, which is crucial for the role.

Prepare for Technical Questions

Expect technical questions related to core AI concepts and model behaviour. Brush up on topics like neural networks, training vs inference, and numerical precision. Being well-prepared will help you answer confidently and demonstrate your expertise.