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# Senior / Staff Machine Learning Engineer, Vehicle Dynamics

- **Company:** WAABI PARTNERS
- **Location:** Dallas, TX, United States
- **Experience:** Expert
- **Salary:** $241,000.0 - $320,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Dynamical Systems, Python (Programming Language), Machine Learning, Pytorch, Deep Learning, Information Technology, Data Analytics, Data Pipelines
- **Published:** October 23, 2026
- **Apply:** https://www.thejobnetwork.com/job/ab62a8bd-21e4-46ac-a22c-dfecced34533/senior-staff-machine-learning-engineer-vehicle-dynamics

## About the Role

* 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.

## Description

* 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.