AI/Machine Learning Fellowship (6-12 months)

Volkswagen AG • Belmont, CA

4d • Onsite

About The Position

The AI/Machine Learning Fellow will support the perception and machine learning team at the Innovation & Engineering Center California (IECC), applying machine learning to automotive applications including autonomous driving, manufacturing, and material design. The team develops AI solutions using current machine-learning techniques and large datasets. The fellow will help AI researchers and engineers identify learning methods for robust perception and develop sensor-data-processing methods to enable safe, robust automated driving.

This fellowship requires a minimum six-month commitment and is intended for current Master's or PhD candidates, or people whose graduation date was within the previous six months. The stated start date is January 2027. International students and candidates using OPT or CPT cannot be considered at this time.

Requirements

• Currently enrolled in a Master's or PhD program at an accredited university or college, or graduated within the last six months.
• Cumulative GPA of at least 3.0.
• Strong Python programming skills.
• Good experience using Linux.
• Experience with deep-learning frameworks such as TensorFlow and PyTorch.
• Good knowledge of image processing and machine learning.
• Ability to work independently, show initiative and motivation, and work as part of a team.

Nice To Haves

• Experience with data gathering, data quality, system architecture, and coding best practices.
• Ability to take initiative to launch projects, learn quickly, and develop ideas in an interdisciplinary environment.
• Ability to extract high-level logical structure from low-level, multidimensional data.
• Experience with Transformers, CNNs, RNNs, LSTMs, and current research trends.
• Knowledge of graphical models.
• Experience with object detection, NLP, or scene understanding.
• Passion for cars or physical products.

Responsibilities

• Pre-process driving data, including checking labels and formatting.
• Support the project team in building data pipelines for training deep neural networks.
• Benchmark neural-network models and report their performance.
• Research network architectures for time-series prediction and anomaly detection.
• Research network architectures to improve environmental perception, with a focus on optical sensors.
• Research supervised and unsupervised learning methods to estimate road participants' depth, motion, and velocity.
• Research neural networks for interpreting camera images, including object detection and panoptic segmentation, and for trajectory and motion planning.
• Fuse neural networks to share resources and reduce memory and computation requirements.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level