Senior Engineer, Prediction, Planning and Controls. Autonomy in London

About Us

Rivianis on a mission to keep the world adventurous forever.

This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract.

As a company, we constantly challenge what’s possible, never simply accepting what has always been done.

We reframe old problems, seek new solutions and operate comfortably in areas that are unknown.

Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations.

Responsibilities

• Design and implementation of learning-based prediction and planning systems, including end-to-end models for real-world driving.
• Develop hybrid planning architectures that combine transformer-based prediction/planning with classical methods such as MPC and graph-based planning.
• Drive end-to-end ownership across data collection, dataset curation, model training, optimization, deployment, and closed-loop evaluation.
• Define and enforce safety and feasibility strategies for trajectory generation, including fallback behaviors and runtime safeguards.
• Partner with Perception and Systems teams to integrate planning models into production-like stacks and ensure robust interface contracts.
• Improve real-time inference performance for in-vehicle deployment using model optimization techniques and toolchains such as Tensor RT.
• Build and scale evaluation pipelines that connect offline metrics with closed-loop behavior in simulation and on-road testing.
• Lead scenario-based validation for rare and safety-critical edge cases, and use results to drive model and system improvements.
• Mentor peers and contribute technical leadership across planning and prediction initiatives.

Qualifications

• Master’s or Ph D 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 Py Torch and deployment acceleration frameworks (for example Tensor RT/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.
• Company Statements
• Equal Opportunity

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