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Senior Staff Software Engineer

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Remote

Senior Staff Software Engineer

Waabi

Senior Staff Software Engineer

Skills

Deep LearningPythonPyTorchMachine LearningAI

1 month ago

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Important information


Salary:

$241,000 – $320,000 / year

Location:

United States

Work mode:

Remote

Published on:

27 September 2026

What they need


Who you are

  • MS/PhD or Bachelor's degree with a minimum of 4 years of industry experience in Robotics, Controls, Mechanical/Electrical Engineering, Computer Science and/or similar technical fields
  • 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

What the job involves

  • 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

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