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.
- Why this job: Make a real impact in AI while collaborating with top minds in a dynamic environment.
- Qualifications: 6+ years in ML engineering, strong Python skills, and experience with major ML frameworks required.
- Other info: Inclusive workplace with opportunities for growth and innovation in cutting-edge AI technology.
The predicted salary is between 48000 - 72000 ÂŁ 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:
- 6+ years of 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.
Senior Staff ML Engineer employer: Cerebras
Contact Detail:
Cerebras Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Staff ML Engineer
✨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 might just get your foot in the door.
✨Tip Number 2
Prepare for technical interviews by brushing up on your ML frameworks and concepts. Practice coding challenges and be ready to discuss your past projects in detail. We want to see your passion and expertise shine!
✨Tip Number 3
Showcase your problem-solving skills! During interviews, be prepared to tackle real-world scenarios related to ML systems. Think aloud and demonstrate how you approach complex issues—this is your chance to impress us!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the Graphcore team. Let’s make it happen!
We think you need these skills to ace Senior Staff ML Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to highlight your experience with machine learning systems and frameworks. We want to see how your skills align with the role, so don’t be shy about showcasing your relevant projects and achievements!
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 our team. Keep it engaging and personal – we love to see your personality come through.
Showcase Your Technical Skills: Don’t forget to mention your hands-on experience with ML frameworks like PyTorch or TensorFlow. We’re looking for someone who can hit the ground running, so highlight any relevant projects or benchmarks you’ve worked on.
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. Plus, it shows us you’re serious about joining our awesome team at Graphcore!
How to prepare for a job interview at Cerebras
✨Know Your ML Frameworks
Make sure you’re well-versed in the major ML frameworks like PyTorch, TensorFlow, or JAX. Be prepared to discuss your hands-on experience with these tools and how you've used them to solve real-world problems.
✨Showcase Your Debugging Skills
Since the role involves debugging functional and performance issues, come ready with examples of challenges you've faced in ML systems. Discuss how you approached these issues and what tools you used to resolve them.
✨Understand AI Concepts Deeply
Brush up on core AI concepts such as neural networks, training vs inference, and performance trade-offs. Being able to articulate these concepts clearly will demonstrate your strong foundation in the field.
✨Prepare for Technical Collaboration
This role requires close collaboration with software and hardware teams. Think of examples where you’ve successfully worked in a team setting, especially in technically demanding environments, and be ready to share those experiences.