Transform your supply chain with Waabi’s AI-first autonomous trucking solution. Scale freight operations, improve safety, and boost efficiency. Learn more. Backed by Khosla.
About the role
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.
What they're looking for
- 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
More about this role
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.
Apply machine learning to improve how the controller adapts across vehicles and operating conditions.
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.
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,...
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