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 open-source contributions.
- 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 required:
- 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
What the job involves:
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
Senior Machine Learning Engineer employer: graphcore
As a Senior Machine Learning Engineer at our company, you will thrive in a dynamic and innovative environment that prioritises collaboration and technical excellence. We offer competitive benefits, a strong focus on employee growth through continuous learning opportunities, and the chance to work with cutting-edge technology alongside a team of passionate professionals. Our inclusive work culture fosters creativity and encourages contributions to open-source projects, making it an ideal place for those seeking meaningful and rewarding employment in the heart of the tech industry.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the ML field, attend meetups, and engage in online forums. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, experiments, and any contributions to open-source frameworks. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on core AI concepts and ML frameworks. Practice explaining your past projects and how you tackled challenges, especially around debugging and performance issues.
✨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, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience with machine learning systems and frameworks. We want to see your hands-on experience with tools like PyTorch or TensorFlow, so don’t hold back on showcasing your projects!
Tailor Your Application:Customise your application to reflect the job description. Use keywords from the listing to demonstrate that you understand what we’re looking for in a Senior Machine Learning Engineer. It shows us you’re serious about the role!
Be Clear and Concise:Keep your application clear and to the point. We appreciate well-structured applications that make it easy for us to see your qualifications and experience. Avoid fluff and focus on what matters!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application 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 implemented them. This will show that you're not just familiar with the theory but have practical skills to back it up.
✨Demonstrate Debugging Skills
Prepare to talk about your analytical and debugging skills. Think of specific examples where you've identified and resolved performance issues in ML systems. Being able to articulate your thought process during these challenges will impress interviewers and highlight your problem-solving abilities.
✨Understand AI Concepts Deeply
Since the role requires a solid understanding of core AI concepts, make sure you can explain key ideas like neural networks, training vs inference, and performance trade-offs. Use real-world examples to illustrate your points, which will demonstrate your depth of knowledge and ability to apply these concepts practically.
✨Showcase Your Collaboration Experience
This position involves working closely with software and hardware teams, so be prepared to discuss your experience collaborating in technically demanding environments. Share examples of how you've worked with cross-functional teams to achieve project goals, as this will show that you're a team player who can thrive in a fast-paced setting.
