Graduate AI Machine Learning Engineer

Atlassian
Wantage, UK
2 days ago
Apply on www.careerjet.co.uk
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Starter
Working hours
Regular working hours

Tech stack

Artificial Intelligence Artificial Neural Networks Big Data Cloud Computing Data Fusion Python (Programming Language) Machine Learning Tensorflow Pytorch Git Scikit Learn Information Technology
+3 more
Atlassian Tools Machine Learning Operations Software Version Control

Job description

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

Requirements

  • 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.

About the company

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.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.careerjet.co.uk
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

6:21 min

Investigating push inefficiencies with upstream Git experts

Jonathan Creamer · Coffee With Developers

2:35 min

Preventing remote code execution in PyTorch models

Balázs Kiss · World Congress 2023

3:28 min

Defining big data and machine learning fundamentals

Ayon Roy · LIVE

1:33 min

Summary of machine learning capabilities and engineering opportunities

Jan Zawadzki · LIVE

56 sec

Favorite git commands and the importance of patch commits

Eileen Uchitelle Eileen Uchitelle +1 · Coffee With Developers

1:23 min

The state of AI adoption in engineering

Alex Laubscher Alex Laubscher +3 · World Congress 2025

Videos

See all

Related articles

See all