Role Summary:

The Engineer I, VLA Planning and Control Deployment (Intern) will improve the planning and control algorithms used inCARIAD Inc’s autonomous-driving deployment stack. Working with planning and deployment engineers, this role willapply operations research and classical control methods to make trajectories more feasible, smooth, andcomputationally reliable. The role focuses on constrained optimization, trajectory refinement, controller performance,and integration of learned model outputs with executable vehicle behavior.

Role Responsibilities:

VLA Model and Development

• Prototype, train, and fine-tune VLA models or supporting perception and world-model components.
• Investigate recent methods and implement a focused research project with mentor guidance.

Data and Experimentation

• Prepare driving datasets and evaluation splits; improve data quality and coverage.
• Run controlled experiments and ablations and maintain reproducible configurations and experiment records.

Evaluation and Integration

• Measure model accuracy, driving behavior, robustness, and inference cost.
• Analyze failures in offline and simulation evaluation; collaborate with planning and deployment engineers on model interfaces.

Documentation and Collaboration

• Document methods, results, and limitations; review code and present findings.
• Deliver a reproducible prototype and a final technical report.

General Skills:

• Analytical thinking and structured problem solving; clear written and verbal communication; careful documentation; collaboration across research and engineering teams.

Required Specialized Skills:

• Foundations in numerical optimization, linear algebra, and dynamical systems.
• Hands-on experience with QP or classical control such as MPC, LQR, or PID; knowledge of constrained
• Ability to implement and debug algorithms in Python or C++ and contribute to deployment code.
• Ability to assess trajectory feasibility, tracking error, constraint violations, and runtime.

Desired Skills:

• Experience formulating QPs with OSQP, qpOASES, or HPIPM
• Knowledge of MPC, trajectory optimization, vehicle dynamics, and lateral / longitudinal control.
• C++, profiling, sparse numerical methods, solver warm starts, or embedded deployment.
• Simulation, log replay, software-in-the-loop, or hardware-in-the-loop validation.

Workplace Flexibility:

• On-site five days a week in Mountain View, CA, following the supplied template. Work arrangement and internship schedule TBD.
• This is an intern W2 position / Hourly rate $44.88 - $96.00
• This role is based in Mountain View, CA. Must be local, no relocation.
• Immediate availability is required. The selected candidate is expected to start promptly upon offer acceptance and pending successful completion of a standard background check and drug screening.

Years of Relevant Experience:

• Coursework, research, or project experience in optimization, motion planning, or control. Prior industry experience is not required.

Required Education:

• Pursuing a degree in Operations Research, Control Engineering, Robotics, Electrical Engineering, Applied Mathematics, or a related field.

Desired Education:

• Master's or PhD study with an emphasis on operations research, optimization, control theory, or vehicle dynamics is preferred.

Compensation

Hourly range is dependent on factors such as geographical differentials, credentials or certifications, industry-based experience, qualification and training. In the city of Mountain View, CA, the hourly range for this position is $44.88 - $69.00.

By submitting your application, you acknowledge and agree to participate in the export control and sanctions compliance screening process. Your cooperation in this matter is essential to our shared success and the integrity of our operations. Thank you for your understanding and commitment to upholding these important standards.