Machine Learning Engineer, Application Software

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here’s what this particular role covers.

🛠️ About our Engineering Teams

The Application Engineering (App SW) team sits within Product & Delivery and works on critical initiatives that push the frontier of model-based autonomous driving. That covers core driving performance as well as feature-level intelligence such as personalisation, comfort and collaboration.

🧠 Your day-to-day

As an ML Engineer, you’ll design and deliver ML-driven behaviours that scale from assisted to autonomous driving. Your work spans model architecture, data pipelines, evaluation frameworks and real-world deployment. You’ll work closely with the AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable and ready for production.

🧩 What you’ll be working on:

- Developing and improving end-to-end driving models with state-of-the-art performance, robustness and generalisation. - Driving projects on personalised and collaborative driving, including behaviour conditioning, comfort tuning and user alignment. - Building evaluation pipelines and metrics for closed-loop and open-loop driving performance and product readiness. - Curating and mining real-world and synthetic data to drive scenario diversity, coverage and feature-specific development. - Influencing architecture choices, training methodologies and deployment pathways for production-scale learning systems. - Collaborating across teams to keep integration smooth and iteration fast. - Mentoring engineers and helping shape the long-term technical direction across Autonomy.

🙌 You should apply if:

Essential:

- Extensive and proven track record of shipping deep learning systems to production. - Expert in deep learning (esp. sequential models, control, planning, or perception). - Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices. - Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components. - Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.