waabi - Senior / Staff Machine Learning Engineer, Vehicle Dynamics

Remote - US & Canada / Toronto, ON / San Francisco, CA / Pittsburgh, PA / Phoenix, AZ / Dallas, TX$241k - $320k+ Equity4w ago

Remote Staff NA Artificial Intelligence Robotics Machine Learning Engineer Software Engineer Staff Engineer Learning & Development C++Python Linear ACCA

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++, 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. • The US yearly salary range for this role is: $241,000 - $320,000 USD in addition to competitive perks & benefits. Waabi US Inc.’s yearly salary ranges are determined based on several factors in accordance with the Company’s compensation practices. Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.

Responsibilities

• 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. • 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.