Senior / Staff Machine Learning Engineer, Vehicle Dynamics
Salary not listed • Remote • Remote US & Canada • Full-time
Posted 1mo ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
Remote Work Policy
Fully remote
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
This role focuses on designing and developing data-driven and machine-learned approaches for vehicle control problems, bringing modern ML to a domain traditionally solved with classical methods. You will develop learned models of vehicle behavior and dynamics, integrate them into closed-loop simulation, and apply machine learning to improve controller adaptation across vehicles and operating conditions. You will own problems end-to-end, from conceptualization and offline experimentation through simulation and on-vehicle validation, and build necessary data pipelines, evaluation metrics, and tooling to measure performance against classical baselines. This is an opportunity to be part of a multidisciplinary team using an AI-first approach to enable safe self-driving at scale and define how learning and classical control coexist in a safety-critical stack.
Responsibilities
- Design and develop data-driven and machine-learned approaches for vehicle control.
- Develop learned models of vehicle behavior and dynamics.
- Integrate learned models into closed-loop simulation.
- Apply machine learning to improve controller adaptation.
- Own problems end-to-end, from conceptualization to on-vehicle validation.
- Build data pipelines, evaluation metrics, and tooling for performance measurement.
- Participate in technical and architecture discussions.
Requirements
- MS/PhD or Bachelors degree with a minimum of 4 years of industry experience in Robotics, Controls, Mechanical/Electrical Engineering, Computer Science, or similar.
- 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.
- Production-quality coding skill in Python and C++.
- 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 design experiments.
- Open-minded and collaborative team player.
- Passion for self-driving technologies and creating innovative solutions.