Senior Engineer, Prediction, Planning and Controls. Autonomy
On-siteLondon, England, United Kingdom
Job Summary
Design and implement learning-based prediction and planning systems, including end-to-end models for real-world driving. Develop hybrid architectures combining transformer-based prediction with classical methods like MPC and graph-based planning. Drive end-to-end ownership across data collection, model training, optimization, deployment, and closed-loop evaluation. Define safety strategies for trajectory generation and partner with Perception and Systems teams to integrate models into production stacks. Improve real-time inference using TensorRT and build evaluation pipelines connecting offline metrics to on-road testing. Lead scenario-based validation for safety-critical edge cases and mentor peers on technical initiatives.
Required Qualifications
- Master's or PhD in Robotics, Control Engineering, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field
- Strong background in autonomous driving prediction and planning, with hands-on experience in both research and deployment
- Deep knowledge of trajectory prediction and planning methods, including transformer-based architectures and sequence modeling
- Strong fundamentals in control and robotics, including MPC, graph search, state estimation, and vehicle behavior modeling
- Proficiency in Python and C++ for production-quality software development
- Hands-on experience with PyTorch and deployment acceleration frameworks (for example TensorRT/ONNX) in real-time systems
- Experience with ROS/Autoware-style robotics integration and simulation-based development workflows
- Strong understanding of open-loop vs closed-loop evaluation, KPI design, failure analysis, and safety-driven model validation
- Demonstrated technical leadership, cross-functional collaboration, and ability to deliver under tight product timelines
- Excellent communication and problem-solving skills in fast-paced, multidisciplinary environments
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