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 Team

You will join a growing team of computer science and robotics experts leveraging machine learning, data and cloud infrastructure to build and deploy powerful on-vehicle reasoning capabilities. Your work will enable Oxa Driver™ to plan and execute sophisticated, safe driving behaviours scaleably across all of our customer domains. As a Senior Engineer (ML Reasoning) you will be taking a leading role within research and development of your team to enable Oxa Driver’s data driven reasoning capabilities. You will actively be training, evaluating, and deploying state-of-the-art machine learning models to reason and plan how to drive in industrial environments.

Key Responsibilities

• Researching, developing, and deploying state-of-the-art machine learning models for autonomous vehicle trajectory planning, specifically utilizing Machine Learning techniques such as Behaviour Cloning (BC) and Reinforcement Learning (RL).
• Designing and scaling end-to-end pipelines for large-scale model training, ensuring efficient distributed training performance across simulation and real-world datasets.
• Applying strong experiment and data analysis skills to rigorously evaluate model performance, turn results into actionable items, and communicate findings with your team.
• Developing simulation-in-the-loop training and evaluation environments, defining rigorous safety/comfort metrics and test scenarios for planning performance analysis.
• Keeping up with the latest advances in imitation learning, deep reinforcement learning, and motion planning research, and applying relevant techniques to Oxa Driver.
• Optimising ML models and codebases for compute and memory efficiency to ensure efficiency in model training pipelines and meet vehicle deployment constraints.
• Understanding what data is needed to train and evaluate ML-based trajectory planning, and working with data teams and tools to source or generate the necessary real or synthetic data to achieve the team’s goals.
• You will be encouraged to share your ideas with the team and the wider business.
• You will interact with other teams to learn about the autonomy system and gain exposure to all aspects of the business.