Engineer , VLA Planning and Control Deployment; Intern

Mountain View, CA

We are CARIAD, an automotive software development team with the Volkswagen Group. Our mission is to make the automotive experience safer, more sustainable, more comfortable, more digital, and more fun. To achieve that we are building the leading tech stack for the automotive industry and creating a unified software platform for over 10 million new vehicles per year. We’re looking for talented, digital minds like you to help us create code that moves the world.

Together with you, we’ll build outstanding digital experiences and products for all Volkswagen Group brands that will transform mobility. Join us as we shape the future of the car and everyone around it.

We are CARIAD, an automotive software development team with the Volkswagen Group. Our mission is to make the automotive experience safer, more sustainable, more comfortable, more digital, and more fun. To achieve that we are building the leading tech stack for the automotive industry and creating a unified software platform for over 10 million new vehicles per year.

We’re looking for talented, digital minds like you to help us create code that moves the world. Together with you, we’ll build outstanding digital experiences and products for all Volkswagen Group brands that will transform mobility. Join us as we shape the future of the car and everyone around it.

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

• 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; improve data quality and coverage.
• Run controlled experiments and ablations and 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.

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

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

• 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 Pythonor C++ and contribute to deployment code.
• Ability to assess trajectory feasibility, tracking error, constraint violations, and runtime.

• 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.

• On‑site five days a week in Mountain View, CA,…