Design and develop data-driven and machine-learned approaches to vehicle control problems, bringing modern ML to a domain traditionally solved with classical methods.

Develop learned models of vehicle behavior and dynamics, and integrate them into the closed-loop simulation.

Apply machine learning to improve how the controller adapts across vehicles and operating conditions.

Be part of a team of multidisciplinary Engineers and Research Scientists using an AI-first approach to enable safe self-driving at scale.

Own problems end to end, from conceptualization and offline experimentation through simulation and on-vehicle validation.

Build the data pipelines, evaluation metrics, and tooling needed to measure whether a learned approach outperforms the classical baseline.

Participate and share ideas in technical and architecture discussions, helping define how learning and classical control coexist in a safety-critical stack.

MS/PhD or Bachelors degree with a minimum of 4 years of industry experience in Robotics, Controls, Mechanical/Electrical Engineering, Computer Science and/or similar technical field(s) of study.

Demonstrated depth in control theory and dynamic systems (e.g., MPC, optimal control, state estimation, system identification, kinematic and dynamic vehicle modeling).

Hands-on experience applying machine learning to a physical system, with real hardware in the loop rather than simulation alone.

Production-quality coding skill in Python and C++, and experience with deep learning frameworks such as PyTorch.

Solid problem solving skills using linear algebra, optimization, statistics & probability.

Ability to rapidly prototype and test new algorithms, and to design the experiments that prove whether they work.

Open-minded and collaborative team player with the willingness to help others.

Passionate about self-driving technologies, solving hard problems, and creating innovative solutions.