Senior / Staff Software Engineer, ML-based Controls (AI)

Location: Hybrid, remote US & Canada, with locations in Toronto, San Francisco, Pittsburgh, Phoenix, and Dallas

Company

Develops AI-first technology for safe self-driving at scale.

What you will do

• Design and develop data-driven and machine-learned approaches to vehicle control problems.
• Develop learned models of vehicle behavior and dynamics and integrate them into closed-loop simulation.
• Apply machine learning to improve controller adaptation across vehicles and operating conditions.
• Own work from conceptualization and offline experimentation through simulation and on-vehicle validation.
• Build data pipelines, evaluation metrics, and tooling to compare learned approaches with classical baselines.
• Contribute to technical and architecture discussions about combining learning and classical control in a safety-critical stack.

Requirements

• MS, PhD, or Bachelor’s degree with at least 4 years of industry experience in robotics, controls, mechanical or electrical engineering, computer science, or a related technical field.
• Strong knowledge of control theory and dynamic systems, including MPC, optimal control, state estimation, system identification, and vehicle modeling.
• Hands-on experience applying machine learning to physical systems with real hardware in the loop.
• Production-quality Python and C++ skills and experience with deep learning frameworks such as PyTorch.
• Strong problem-solving skills using linear algebra, optimization, statistics, and probability.
• Ability to rapidly prototype algorithms and design experiments that evaluate their performance.

Nice to have

• Experience with self-driving technologies and vehicle control systems.
• Experience working collaboratively across engineering and research disciplines.

Culture & Benefits

• Work in a multidisciplinary group of engineers and research scientists.
• Collaborate on an AI-first approach to autonomous driving.
• Contribute ideas in technical and architecture discussions.
• Help solve complex problems in a safety-critical domain.