Data Engineer - ML Systems Autonomous Driving

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.

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.

• 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)

• 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

• Strong software engineering skills, with Python as a primary language
• Strong SQL skills and experience working with analytical data warehouses (e.g. Big Query, 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

• Experience…