Job Description

As a Controls Software Intern, you will deliver critical improvements and features for our autonomy stack. You will be working alongside engineers, research scientists, and domain experts to build optimal and data driven controls to realize planned vehicle trajectories.

Responsibilities:

• Design, implement, and enhance control algorithms by integrating MPC with learning-based approaches.
• Migrate the Control QP solver and benchmark its performance.
• Develop online adaptation techniques to manage vehicle behavior drift.
• Analyze trade-offs between online learning and offline model updates.
• Evaluate simulation capabilities by designing the regression test suite.

Required Skills:

• Pursuing MS or PhD in Robotics, Computer Science, or related field.
• Hands-on application skills in any of the following areas: adaptive and nonlinear control, MPC & optimal control, robust control, data-driven control, Kalman filters, etc.
• Strong foundation in motion control and modern neural network architectures, with expertise in at least one application area, such as IL/RL, time-series analysis, or dynamic system modeling.
• Skilled in debugging robotic systems within Linux environments, with strong programming expertise in Python and C++

Preferred Skills:

• Experience with Imitation Learning, Reinforcement Learning, modern Neural Network architectures.
• Experience in model development & training with modern frameworks (e.g., PyTorch).

$19 - $65 an hour

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