At a Glance
- Tasks: Build data systems for training machine learning in autonomous driving.
- Company: Join Oxa, a leader in autonomous vehicle technology.
- Benefits: Competitive salary, flexible working, and health benefits.
- Why this job: Make an impact in the exciting field of self-driving technology.
- Qualifications: Strong Python and SQL skills; experience with data pipelines.
- Other info: Diverse team culture that values innovation and authenticity.
The predicted salary is between 60000 - 80000 £ per year.
Who are we? Founded in 2014, Oxa is a global leader in autonomous vehicle (AV) technology, dedicated to accelerating Industrial Mobile Autonomy (IMA). We develop advanced physical AI and robotics technology, anchored around our configurable and explainable self-driving software, Oxa Driver; development toolchain, Oxa Foundry; and fleet management software, Oxa Hub. We utilise hardware blueprints known as Reference Autonomy Designs (RADs) to enable the integration of sensors, compute and drive-by-wire systems into existing vehicles produced by OEMs. Our solutions automate repetitive industrial driving tasks, such as the towing and carrying of goods in locations like ports, airports and manufacturing facilities, or asset and perimeter monitoring in environments such as solar farms or industrial plants. We’re helping global businesses to address critical challenges like labour shortages and rising operational costs - driving efficiency, productivity, and safety. Based in Oxford, and with offices in Canada, our engineering team is drawn from the world’s top physical AI specialists and led by originators of the field.
Your Role: We are hiring a Data Engineer to help build the systems that prepare, curate, and scale training and evaluation data for machine learning in autonomous driving. You will work across the full data lifecycle, from raw vehicle logs and simulation outputs to curated, labelled, and model-ready datasets. This includes handling multimodal sensor data, scaling labelling through both human and ML-based workflows, and enabling intelligent selection of high-value data from thousands of hours of real-world and simulated driving. This role sits close to model performance and safety ensuring quality, structure, and selection of data directly influence how perception and planning systems behave in the real world.
What You Will Work On: You will work on systems that:
- Transform raw multimodal logs (camera, LiDAR, radar) into training-ready datasets
- Support hand-labelled and auto-labelled data pipelines, including validation and quality control
- Help build and scale autolabelling systems, where ML models generate annotations across large datasets
- Support intelligent data curation and selection from thousands of hours of real-world and simulated driving
- Generate and process simulated data for perception and planning, ensuring sufficient sim-to-real fidelity for synthetic data to be useful in training and evaluation
- Manage multiple data representations, including sensor-native formats (images, point clouds), structured scene representations (objects, semantics, occupancy), and bird’s-eye view (BEV) representations for downstream models
- Support dataset generation for perception models (for example detection, segmentation, and occupancy) and planning models (behavioural learning)
Key Responsibilities:
- Design, build, and maintain scalable data pipelines from raw logs to training datasets
- Contribute to systems for dataset generation, versioning, and reproducibility
- Develop and operate autolabelling pipelines, integrating model outputs into labelling workflows
- Implement quality control mechanisms for both human and ML-generated labels
- Support ML-assisted data curation workflows to identify high-value or failure-prone scenarios
- Build pipelines to generate, transform, and validate simulated datasets, helping identify and reduce sim-to-real mismatches to improve their usefulness for training and evaluation
- Work closely with ML engineers to translate model requirements into data pipelines and datasets
- Debug data issues across the stack, from sensor-level artefacts to dataset inconsistencies
- Improve storage, compute, and throughput efficiency for large-scale datasets
What You Need to Succeed:
- Strong software engineering skills, with Python as a primary language
- Strong SQL skills and experience working with analytical data warehouses (e.g. BigQuery, Snowflake)
- Experience building production-grade data pipelines or distributed data systems
- Experience working with large-scale datasets
- Familiarity with cloud infrastructure (e.g. GCP, AWS, or similar)
- Solid understanding of data modelling, transformation, and data quality considerations
Extra Kudos If You Have:
- Experience working with ML data pipelines or supporting ML systems
- Familiarity with computer vision, robotics, or autonomous systems
- Experience working with multimodal sensor data, such as images, LiDAR, or radar
- Exposure to labelling workflows, autolabelling, or dataset curation
- Experience with spatial or geospatial data
- Familiarity with Linux-based development environments
- Experience with tools such as Docker, shell scripting, workflow orchestrators, and transformation frameworks (e.g. Hera Workflows, dbt)
Benefits:
- Competitive salary, benchmarked against the market and reviewed annually
- Company share programme
- Hybrid and/or flexible remote working arrangements
- Core benefits of market leading private healthcare, life assurance, critical illness cover, income protection, alongside a company paid health cash plan (including gym discounts)
- A salary exchange pension plan
- 25 days’ annual leave plus bank holidays
- A pet-friendly office environment
- Safe assigned spaces for team members with individual and diverse needs
Our Culture: We are on a mission to unlock the benefits of self-driving technology to every person and organisation on the planet. We are creating an environment where everyone, from any background, can do their best work which, put simply, is the right thing to do. We hire and nurture those we can learn from, valuing diversity and the innovation that this drives. We promote an open and inclusive culture that empowers our Oxbots to bring their whole, authentic selves to work every day.
Why become an Oxbot? Our team of experts in computer science, AI, robotics and machine learning is world-class, and together they’re solving the most exciting and important technological challenges of our times. Our diverse, multi-cultural crew is guided by a shared vision to bring the myriad benefits of autonomy to our customers and partners. And in a company that celebrates uniqueness as much as skill and experience, we do it with energy, conviction and a healthy dose of excitement, too. If you are bold, creative and hyper skilled, come and create the future of autonomy with us at Oxa.
Data Engineer - ML Systems for Autonomous Driving employer: Oxa
Contact Detail:
Oxa Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer - ML Systems for Autonomous Driving
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with Oxa employees on LinkedIn. A friendly chat can sometimes lead to opportunities that aren’t even advertised!
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects related to data engineering and machine learning. This gives us a tangible way to see what you can do beyond your CV.
✨Tip Number 3
Prepare for the interview by brushing up on your technical skills. Be ready to discuss your experience with data pipelines, SQL, and any relevant tools. We love seeing candidates who can talk the talk and walk the walk!
✨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, it shows you’re genuinely interested in joining our team at Oxa.
We think you need these skills to ace Data Engineer - ML Systems for Autonomous Driving
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Engineer role. Highlight your experience with Python, SQL, and any relevant data pipeline projects. We want to see how your skills align with what we do at Oxa!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for autonomous driving and how your background makes you a great fit for our team. Let us know why you want to join Oxa specifically.
Showcase Relevant Projects: If you've worked on any projects involving ML data pipelines or multimodal sensor data, make sure to mention them. We love seeing real-world applications of your skills, so don’t hold back!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're keen on joining our team!
How to prepare for a job interview at Oxa
✨Know Your Data Inside Out
Before the interview, dive deep into the types of data you'll be working with, especially multimodal sensor data like camera, LiDAR, and radar. Be prepared to discuss how you would transform raw logs into training-ready datasets and share any relevant experiences you've had with similar data.
✨Showcase Your Software Skills
Since strong software engineering skills are crucial for this role, brush up on your Python and SQL knowledge. Be ready to talk about specific projects where you've built production-grade data pipelines or worked with analytical data warehouses. Highlight any experience with cloud infrastructure too!
✨Understand the Bigger Picture
Familiarise yourself with the company's mission and how your role as a Data Engineer fits into the development of autonomous driving technology. Think about how your work will impact model performance and safety, and be ready to discuss how you can contribute to these goals.
✨Prepare Questions That Matter
Interviews are a two-way street! Prepare insightful questions about the team dynamics, the technologies they use, and their approach to data quality and validation. This shows your genuine interest in the role and helps you assess if it's the right fit for you.