Graduate AI Machine Learning Engineer
Ref:
R14415
- Full time
- Grove, Wantage
- Early Careers
- Technology
- Graduate (Fixed Term)
Join our Talent Community
Join our talent community to stay connected with life on and off the track at Atlassian Williams F1 Team.As part of our talent pool, you’ll be the first to hear about opportunities that match your skillset - your chance to be part of something extraordinary and help create our winning formula.
RegisterMeet Eoghain McLaughlin working in Technology & Innovation
At Atlassian Williams F1 Team, our two-year Graduate Programme throws you into real motorsport from day one. Our rotational routes offer broad, cross-team exposure, or you can go deep as a subject-matter expert, specialising in one area. Expect live projects, a leadership speaker series, and wraparound support from our Early Careers team and your manager. Join a tight-knit cohort, build a network across the business, and fast-track your skills into impact. This could be your launchpad to become one of our future leaders.
At Atlassian Williams F1 Team, the Technology & Innovation Group (TIG) is modernising our tech to sharpen our Formula 1 advantage. Leveraging data, AI, and cutting-edge software, we speed up development and enhance performance. In a budget-capped sport, smart technology is key to maximising efficiency and every fraction of performance.
As a graduate in this role, you help build the intelligent systems that drive performance on and off the track. You apply machine learning, data science, statistics and simulation to real motorsport problems, from vehicle dynamics to race strategy. It is hands-on work where your models feed directly into how the car is developed and run.
- Help design and train AI models for vehicle performance, aerodynamics, race strategy and operations
- Work with everything from statistical models to deep neural networks on real engineering problems
- Build machine learning pipelines for simulation, data fusion and predictive analytics
- Use frameworks such as PyTorch, Scikit-learn and Polars to draw insight from large datasets
- Work across departments to put models into car development, showing innovation and teamwork
History doesn’t repeat itself. It’s rebuilt - one breakthrough at a time. Be part of the team redefining performance and writing the next winning chapter for Atlassian Williams F1 Team.
Atlassian Williams F1 Team– Shape our team's next chapter:
For nearly 50 years, Williams F1 Team has pushed the limits of speed and innovation. As one of the most successful teams in F1 history, with 16 World Championships and a legacy shaped by legends like Sir Frank Williams and Nigel Mansell, we’re now on a bold mission to reclaim our place at the front and win multiple championships again.
Our People Promise:
Join a high-performance team where ambition and our people promise guide everything we do. In a fast-paced, challenging environment, your work directly shapes our winning formula. Collaborate with the brightest minds in motorsport, building on our rich heritage and pushing the boundaries of speed and innovation. We want people who live our values - innovation, teamwork, resilience, excellence, and accountability to create the winning formula that drives Williams F1 Team back to the top.
- A degree in Computer Science, Maths, Physics, Engineering, Statistics or similar
- Proficiency in Python and ML frameworks (e.g., PyTorch, Scikit-learn, TensorFlow).
- Some exposure to applying AI techniques and models to improve performance
- Familiarity with cloud platforms and version control systems (e.g., Git).
- Knowledge of motorsport or automotive engineering is advantageous but not essential.
Life at Williams
Based at our Grove campus in Oxfordshire, enjoy competitive benefits, exclusive events, subsidised dining, special car schemes, and 24/7 gym access with fitness classes. Need a break? Relax in green spaces or explore nearby Oxford city centre, just 30 minutes away by bus.
Atlassian Williams F1 Team is an equal opportunity employer that values diversity and inclusion. We are happy to discuss reasonable job adjustments.
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