Senior ML QA Engineer - Benchmark & Validate AI Stack

Senior ML QA Engineer - Benchmark & Validate AI Stack

Full-Time 60000 - 80000 £ / year (est.) No working from home possible
graphcore

At a Glance

  • Tasks: Benchmark and validate cutting-edge ML models and frameworks for performance and reliability.
  • Company: Join Graphcore, a leader in AI compute backed by SoftBank Group.
  • Benefits: Enjoy flexible working, generous leave, private medical insurance, and a vibrant office culture.
  • Other info: Be part of a diverse team committed to innovation and inclusivity.
  • Why this job: Make a real impact in the fast-evolving world of AI technology.
  • Qualifications: Experience in ML systems, strong Python skills, and a solid understanding of AI concepts.

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

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

  • 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.
  • 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.

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. We take pride in our commitment to creating an inclusive and diverse workplace.

Senior ML QA Engineer - Benchmark & Validate AI Stack employer: graphcore

Graphcore is an exceptional employer, offering a dynamic work environment in the heart of Bristol where innovation thrives. With a strong focus on 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 in the rapidly evolving field of artificial intelligence, backed by the resources of the SoftBank Group.

graphcore

Contact Details:

graphcore Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Senior ML QA Engineer - Benchmark & Validate AI Stack

Tip Number 1

Network like a pro! Reach out to folks in the industry, attend meetups, and connect with Graphcore employees on LinkedIn. A personal touch can make all the difference when it comes to landing that interview.

Tip Number 2

Show off your skills! Create a portfolio showcasing your ML projects, benchmarks, or any cool experiments you've done. This gives you a chance to demonstrate your hands-on experience and passion for the field.

Tip Number 3

Prepare for technical interviews by brushing up on your ML concepts and debugging skills. Practice coding challenges and be ready to discuss your past experiences with ML frameworks like PyTorch or TensorFlow.

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 the Graphcore team.

We think you need these skills to ace Senior ML QA Engineer - Benchmark & Validate AI Stack

Machine Learning Systems
AI Concepts
Model Behaviour Understanding
Performance Testing
Reliability Testing
Correctness Validation
ML Frameworks (e.g. PyTorch, TensorFlow, JAX)

Some tips for your application 🫡

Tailor Your CV:Make sure your CV is tailored to the role of Senior ML QA Engineer. Highlight your experience with machine learning systems, frameworks, and any relevant projects that showcase your skills in testing and validating AI models.

Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a perfect fit for Graphcore. Don’t forget to mention specific experiences that relate to the job description.

Showcase Your Technical Skills:Be sure to include your hands-on experience with ML frameworks like PyTorch or TensorFlow. Mention any tools or scripts you've developed in Python that relate to testing and benchmarking, as this will show us your practical knowledge.

Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands and shows us you’re serious about joining our team at Graphcore!

How to prepare for a job interview at graphcore

Know Your ML Frameworks

Make sure you’re well-versed in the major ML frameworks like PyTorch, TensorFlow, or JAX. Brush up on their functionalities and be ready to discuss how you've used them in past projects, especially in terms of testing and validating models.

Understand AI Concepts Inside Out

Familiarise yourself with core AI concepts such as neural networks, training vs inference, and performance trade-offs. Be prepared to explain these concepts clearly, as they are crucial for the role and will likely come up during technical discussions.

Showcase Your Debugging Skills

Be ready to share specific examples of how you've tackled debugging challenges in ML systems. Highlight your analytical skills and any tools you’ve used for performance analysis or numerical accuracy validation.

Prepare for Collaboration

Since this role involves working closely with software and hardware teams, think of examples that demonstrate your collaborative spirit. Discuss how you’ve contributed to team projects, especially in fast-paced environments, and how you ensure adequate test coverage.