Sensor Technology Fellowship (6-12 months)

Volkswagen AG•Belmont, CA

About The Position

As MOIA America, we develop and realize fully autonomous mobility and transportation services. Our mission is to make mobility-and transportation-as-a-service safe, accessible and most attractive for society. For that, we cover the entire ground from strategy and business development, software development and end-2-end integration, fleet operations to next-generation self-driving systems. Being the driver in Volkswagen Group initiative for mobility solutions, we’re an integral part of Volkswagen Group's future success. We are looking for a motivated intern to join our Autonomous Driving Sensor team. You will support the evaluation and benchmarking of perception sensors — primarily LiDAR, radar, and camera — used in autonomous driving applications. This is a hands-on engineering role where you will work directly with sensor hardware, recorded datasets, and processing pipelines to characterize sensor performance under real-world conditions.

Requirements

Currently pursuing a degree (Bachelor's or Master's) in Electrical Engineering, Computer Science, Physics, Robotics, or a related technical field. If you are pursuing a Bachelor's degree, you must have senior standing at a minimum.
You must have a 3.0 GPA (Transcripts are required for consideration)
Familiarity with Python for data analysis and scripting (NumPy, pandas, matplotlib or similar)
Familiarity with Ubuntu
Background with Ethernet and serial communication
Basic knowledge of C++ (reading and modifying existing codebases)
Foundational understanding of at least one sensor modality (LiDAR, radar, or camera) — operating principles, key parameters, and typical limitations
Ability to work with 3D point cloud data (coordinate systems, transformations, filtering)
Strong analytical mindset and attention to detail when interpreting measurement data
Hands-on experience with LLM-assisted workflows (e.g., Claude, ChatGPT, GitHub Copilot/Codex) for coding, analysis, or technical writing — you actively use AI tools to work faster and smarter

Nice To Haves

Experience with point cloud libraries (Open3D, PCL, or similar)
Familiarity with sensor data formats (PCD, rosbag, MF4, PCAP)
Exposure to radar signal processing concepts (Doppler, CFAR detection, range-velocity ambiguity)
Knowledge of image processing fundamentals (ISP pipeline, lens distortion, HDR)
Understanding of sensor fusion concepts and coordinate frame calibration
Experience with Git, Linux, and automated testing workflows
Familiarity with MCP (Model Context Protocol) or building custom AI tool integrations

Responsibilities

Conduct systematic sensor assessments covering detection range, resolution, field of view, and accuracy across environmental conditions (weather, lighting, temperature)
Process and analyze 3D point cloud data from LiDAR and radar sensors to extract performance metrics such as point density at range, reflectivity response, and angular resolution
Define and compute sensor KPIs; Integrate new KPIs in assessment pipeline
Develop and maintain Python-based tooling for automated sensor data evaluation and reporting
Translate sensor-level measurement results into system-level context: map component performance (e.g., detection range, angular accuracy) to Self-Driving System (SDS) requirements and identify gaps or margins
Support test campaign planning, including scenario definition, and environmental condition coverage
Document results in structured test reports and contribute to sensor selection decisions
Leverage modern AI tools (Claude, Codex, MCP integrations) to accelerate data analysis, code development, and documentation — and help the team adopt AI-native workflows