At a Glance
- Tasks: Test and validate cutting-edge ML software, ensuring performance and reliability across AI workloads.
- Company: Join a leading tech firm focused on innovation and collaboration in AI.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on automation and high-quality product delivery.
- Why this job: Make a real impact in the AI field while working with state-of-the-art technologies.
- Qualifications: Strong experience in ML systems, Python proficiency, and analytical skills required.
The predicted salary is between 60000 - 80000 £ per year.
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 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.
- 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.
- (Desirable) Familiarity with performance analysis, profiling tools, or numerical accuracy validation.
- (Desirable) Exposure to distributed training or inference systems.
- (Desirable) Experience with hardware-accelerated ML, compilers, or system-level performance considerations.
- (Desirable) Familiarity with CI/CD systems used for ML workflows.
- (Desirable) Experience contributing to open-source ML frameworks or tooling.
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 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.
- 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.
Staff Machine Learning Engineer in London employer: graphcore
As a Staff Machine Learning Engineer at our company, you will thrive in a dynamic and innovative work culture that prioritises collaboration and continuous learning. We offer competitive benefits, including professional development opportunities and a supportive environment that encourages creativity and technical excellence, all while being located in a vibrant tech hub that fosters networking and growth in the AI field.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A personal connection can often get your foot in the door faster than a CV.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, experiments, and any contributions to open-source frameworks. This gives potential employers a taste of what you can do.
✨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 in detail.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it’s a great way to stay updated on new opportunities.
We think you need these skills to ace Staff Machine Learning Engineer in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience with machine learning systems and frameworks in your application. We want to see your hands-on experience with tools like PyTorch or TensorFlow, so don’t hold back!
Be Specific About Your Experience:When discussing your background, be specific about the projects you've worked on. We love details! Share how you’ve tackled debugging issues or optimised performance in ML systems.
Tailor Your Application:Don’t just send a generic application. Tailor it to our job description! Mention your familiarity with AI concepts and any experience with MLOps or CI/CD systems that could set you apart.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and get back to you quickly!
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 challenges you've faced while debugging or optimising models.
✨Understand Core AI Concepts
Familiarise yourself with essential AI concepts such as neural networks, training vs inference, and performance trade-offs. Being able to explain these concepts clearly will show that you have a solid foundation in the field.
✨Showcase Your Analytical Skills
Prepare to discuss specific examples where you've analysed model behaviour or system performance. Highlight any experience you have with performance analysis tools or debugging techniques, as this will demonstrate your analytical prowess.
✨Collaborate and Communicate
Since the role involves working closely with software and hardware teams, be ready to talk about your collaboration experiences. Share examples of how you've effectively communicated technical information to non-technical team members or stakeholders.
