Motional is looking to deploy, optimize, and maintain machine learning-based motion planning and control algorithms for real-time autonomous driving on vehicle platforms, ensuring reliability under strict performance and safety constraints.
Requirements
- Strong software engineering skills in C++ and Python
- Hands-on experience with ML frameworks (PyTorch, TensorFlow) and model optimization for deployment.
- Familiarity with GPU acceleration, or inference optimization (e.g., TensorRT, CUDA).
- Experience with autonomous vehicle motion planning, control algorithms (MPC, LQR, PID), or reinforcement learning–based methods.
- Experience with ROS, AUTOSAR, or other real-time robotics frameworks.
- Knowledge of numerical optimization and its applications in trajectory generation.
Responsibilities
- Deploy ML-based motion planning and control models onto vehicle platforms, ensuring performance under resource constraints.
- Optimize models for inference speed, latency, and memory footprint without sacrificing accuracy or safety.
- Collaborate with motion planning, controls, and perception teams to integrate ML components into the end-to-end autonomous driving stack.
- Build scalable deployment infrastructure including evaluation pipelines, model packaging, benchmarking, and automated validation.
- Validate model performance in both simulation and on-road testing, analyzing results and driving iterative improvements.
- Maintain production-quality code in C++ and Python
Other
- 4+ years of professional experience deploying ML systems in real-world robotics, embedded, or autonomous platforms.
- Strong problem-solving skills and ability to debug complex systems under production constraints.
- BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
- Knowledge of modern development practices (code reviews, testing, CI/CD).
- Publications in relevant ML or robotics conferences (ICRA, NeurIPS, CoRL, RSS, etc.).
