Engineer I, VLA Planning and Control Deployment (Intern)
About The Position
The Engineer I, VLA Planning and Control Deployment (Intern) will improve planning and control algorithms used in CARIAD Inc.’s autonomous-driving deployment stack. Working with planning and deployment engineers, the intern will apply operations research and classical control methods to make trajectories more feasible, smooth, and computationally reliable. The work focuses on constrained optimization, trajectory refinement, controller performance, and integration of learned model outputs with executable vehicle behavior. The role also involves developing and evaluating VLA models or supporting perception and world-model components, preparing driving datasets, conducting experiments, assessing model and driving performance, and collaborating on model interfaces. The intern will document methods, results, and limitations, review code, present findings, and deliver a reproducible prototype and final technical report. This is an on-site W2 internship in Mountain View, California, five days per week. The work arrangement and internship schedule are TBD. The candidate must be local, as relocation is not available, and immediate availability is required. The selected candidate is expected to start promptly after offer acceptance and successful completion of a standard background check and drug screening. Hourly compensation is listed as $44.88–$96.00 in the role description; the Mountain View range is stated as $44.88–$69.00, depending on factors such as location, credentials, experience, qualifications, and training. CARIAD is an automotive software development team within the Volkswagen Group, building software and digital experiences for its vehicle brands.
Requirements
- Foundations in numerical optimization, linear algebra, and dynamical systems.
- Hands-on experience with quadratic programming (QP) or classical control methods such as MPC, LQR, or PID.
- 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.
- Coursework, research, or project experience in optimization, motion planning, or control; prior industry experience is not required.
- Pursuing a degree in Operations Research, Control Engineering, Robotics, Electrical Engineering, Applied Mathematics, or a related field.
- Analytical thinking and structured problem-solving skills.
- Clear written and verbal communication, careful documentation, and ability to collaborate across research and engineering teams.
- Must be local to Mountain View, California; relocation is not available.
- Immediate availability is required.
- Must participate in the company’s export control and sanctions compliance screening process.
Nice To Haves
- Experience formulating QPs with OSQP, qpOASES, or HPIPM.
- Knowledge of MPC, trajectory optimization, vehicle dynamics, and lateral or longitudinal control.
- Experience with C++, profiling, sparse numerical methods, solver warm starts, or embedded deployment.
- Experience with simulation, log replay, software-in-the-loop, or hardware-in-the-loop validation.
- Master’s or PhD study emphasizing operations research, optimization, control theory, or vehicle dynamics.
Responsibilities
- 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.
- Prepare driving datasets and evaluation splits, and improve data quality and coverage.
- Run controlled experiments and ablations; maintain reproducible configurations and experiment records.
- 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.
- Apply optimization and classical control methods to improve trajectory feasibility, smoothness, and computational reliability.
- Assess trajectory feasibility, tracking error, constraint violations, and runtime.
- Document methods, results, and limitations; review code and present findings.
- Deliver a reproducible prototype and a final technical report.
Benefits
- Medical, dental, and vision coverage.
- 401(k) with employer match and a defined contribution plan.
- Short- and long-term disability insurance.
- Basic life and AD&D insurance.
- Employee assistance program.
- Tuition reimbursement and student loan repayment plans.
- Maternity and non-primary caregiver leave.
- Adoption assistance.
- Employee referral program.
- Vacation and paid holidays.
- Vehicle lease program covering registration and insurance fees.
- Performance-based merits and annual bonus.
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What This Job Offers
Career Level
Intern
Education Level
No Education Listed
