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 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
Senior Machine Learning Engineer in Cambridge 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 a culture that encourages creativity and problem-solving. Located in a vibrant tech hub, our team is dedicated to pushing the boundaries of AI technology while ensuring a supportive atmosphere for all employees.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer in Cambridge
✨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 a lead on that perfect job!
✨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. Confidence is key, so let your passion for ML shine through!
✨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 makes it easier for us to keep track of your application!
We think you need these skills to ace Senior Machine Learning Engineer in Cambridge
Some tips for your application 🫡
Show Off Your Experience:When you're writing your application, make sure to highlight your hands-on experience with ML frameworks like PyTorch or TensorFlow. We want to see how you've tackled real-world problems and what you've learned from them!
Be Specific About Your Skills:Don't just list your skills; give us examples! If you've debugged performance issues or worked with MLOps pipelines, share a brief story about it. This helps us understand your thought process and problem-solving abilities.
Tailor Your Application:Make sure your application speaks directly to the job description. Use similar language and keywords that we’ve mentioned, so we can easily see how you fit into our team. It shows us you’re genuinely interested in the role!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
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 and TensorFlow. Brush up on how they work, their strengths and weaknesses, and be ready to discuss your hands-on experience with them during the interview.
✨Demonstrate Your Debugging Skills
Prepare to talk about specific instances where you've debugged functional or performance issues in ML systems. Have examples ready that showcase your analytical skills and how you reasoned about model behaviour and system performance.
✨Familiarise Yourself with CI/CD for ML
Since familiarity with CI/CD systems is desirable, it’s a good idea to understand how these systems integrate into ML workflows. Be prepared to discuss any experience you have with automating testing and deployment processes.
✨Showcase Your Collaboration Experience
This role involves working closely with software and hardware teams, so highlight any past experiences where you collaborated across different teams. Discuss how you contributed to projects and how you ensured effective communication and teamwork.
