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Senior / Staff Software Engineer, ML-based Controls

🕒 17 days ago
Machine LearningVehicle ControlRoboticsControl Theory

📜 Description

  • Design and develop data-driven and machine-learned approaches to vehicle control problems.
  • Develop learned models of vehicle behavior and dynamics, integrating them into closed-loop simulations.
  • Apply machine learning to improve controller adaptability across vehicles and operating conditions.
  • Collaborate with multidisciplinary Engineers and Research Scientists to enable safe self-driving at scale.
  • Own problems end to end, from conceptualization and offline experimentation through simulation and on-vehicle validation.
  • Build data pipelines, evaluation metrics, and tooling to measure performance against classical baselines.

🛠️ 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.
Full job description

You Will…

  • 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.

Qualifications:

  • 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.
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