We are seeking a highly motivated Senior Controls Modeling and Simulation Engineer to join our Autonomy Controls team. The ideal candidate will have a strong background in motion planning, control systems, and vehicle dynamics, with experience in both modeling and simulation. You will play a key role in developing and improving advanced control algorithms and supporting a variety of projects critical to our next generation autonomous vehicle systems.
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Design and implement high-performance control algorithms to solve real-time trajectory optimization and control problems.
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Design and implement optimization-based control strategies using convex and non-convex optimization techniques to solve control problems
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Collaborate with cross-functional teams to deliver improvements in vehicle motion planning, control, and feature engineering for L3/L4 autonomy.
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Optimize computational efficiency of control algorithms for real-time embedded implementation, including algorithm complexity reduction and solver selection.
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Support data analysis, KPI development, and embedded software integration for control features.
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Build and refine vehicle dynamics models to support simulation and validation of control strategies.
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Assist in the creation and maintenance of simulation environments for vehicle dynamics and control system validation.
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Contribute to the development and validation of new features through on-road and simulation-based testing.
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Bachelor's or Master's degree in Mechanical Engineering, Electrical Engineering, Robotics, Computer Science, or a related field.
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Strong background in motion planning, control theory, and vehicle dynamics.
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3+ years of experience in optimization theory and its application to control systems design and proficiency with optimization solvers and frameworks.
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Proficiency in programming languages such as C++ and Python.
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Knowledge of optimization algorithms including gradient descent, interior point methods, and sequential convex programming.
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Familiarity with multi-objective optimization and trade-off analysis for competing control objectives (comfort, safety, efficiency).
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Strong background in machine learning, AI, and robotics, with hands-on experience developing and deploying learning-based algorithms.
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Experience with embedded systems and real-time control implementation is a plus.
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Excellent problem-solving skills and ability to work collaboratively in a fast-paced environment.
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Prior internship or project
