Senior / Staff Software Engineer, ML-based Controls

Waabi•Toronto, ON

52d•$241,000 - $320,000

About The Position

This role involves designing and developing data-driven and machine-learned approaches to vehicle control problems, applying modern ML to a domain traditionally solved with classical methods. The engineer 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. The position is part of a multidisciplinary team using an AI-first approach to enable safe self-driving at scale. Responsibilities include owning problems end-to-end, from conceptualization and offline experimentation through simulation and on-vehicle validation, as well as building data pipelines, evaluation metrics, and tooling to compare learned approaches against classical baselines. The role also involves participating in technical and architecture discussions to define the coexistence of learning and classical control in a safety-critical stack.

Requirements

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++.
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.

Nice To Haves

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

Responsibilities

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 the closed-loop simulation.
Apply machine learning to improve controller adaptation across vehicles and operating conditions.
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.