Join the AI team as a Senior Software Engineer to build and maintain critical systems for AI models, driving impactful projects across data pipelines, infrastructure, and user-facing applications.
Senior / Staff Software Engineer, ML-based Controls
📜 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.
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