Machine Learning Fellowship (6-12 months)

Brief Role Description

At the Innovation & Engineering Center California (IECC), we represent the Volkswagen Group in applied research and development. Located in the heart of Silicon Valley, we create bold new ideas for the Volkswagen, Audi, Bentley, Lamborghini, Bugatti and Porsche brands. We’re a team of engineers, designers, scientists, and psychologists looking to develop innovations for future generations of cars, and to transfer technologies from many industries and research institutions into the automotive domain. Our mission is to drive change which means we are not only impacting one of the world’s largest car makers, but also the lives of millions of people. Are you ready to join us?

*Six Month Minimum commitment  - Masters or PhD candidates only.
We are unable to consider International Students/OPT or CPT at this time.

Machine Learning Fellowship

Role Summary:

The perception and machine learning team is tasked to apply machine learning to the automotive industry. Applications include autonomous driving, manufacturing, material design, etc. The team develops state of the art AI solutions to solve complex and challenging problems by leveraging the latest techniques in machine learning on large data sets. At ICC, you will be involved in developing modern methods in the field of sensor data processing to enable safe and robust automated driving in any scenario. During this fellowship, you will be supporting a team of AI researchers and engineers to find suitable learning methods for robust perception.

Role Responsibilities

Role Responsibilities

• Driving data pre-processing including checking labels, formatting, etc.
• Support project team in building data pipelines for training deep neural networks.
• Benchmarking and reporting of various neural network models performance.
• Research into suitable new network architectures to for time series prediction, and anomalies detection.
• Research into suitable new network architectures to improve the perception of the environment with a focus on optical sensors.
• Research on supervised and unsupervised learning methods to estimate road participants' depth, motion, and velocity. 
• Research on various neural networks for the interpretation of camera images (object detection, panoptic segmentation) and trajectory/motion planning.
• Fusion of different neural networks to use shared resources to decrease memory and computation footprint.

Qualification requirements

• Must be enrolled at a University/College or Graduation date must be within the last six months in a Masters or PhD program.
• Must have a 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.
• Independent work, initiative, motivation, ability to work in a team.

Competencies