Senior AI Engineer (ML/DL) (Unable to consider OPT/CPT candidates)

Worldwide, the Volkswagen Group has a long tradition of dramatic innovations. The Volkswagen Group with its headquarters in Wolfsburg, Germany is one of the world’s leading automobile manufacturers and the largest carmaker in Europe. The Group comprises twelve brands from seven European countries: Volkswagen Passenger Cars, Audi, SEAT, ŠKODA, Bentley, Bugatti, Lamborghini, Porsche, Ducati, Volkswagen Commercial Vehicles, Scania and MAN.

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?

 

Role Summary

This role will be tasked with applying machine learning/deep learning to the automotive industry. Applications could include autonomous driving, manufacturing, design etc. This is more than a purely academic role – you will likely need to integrate the modules you build into real cars and also think about questions around testability and proving safety. Working in our facility requires a high level of independence and an ability to deal with lots of ambiguity. The incumbent will not be given clear cut projects – instead this individual and subsequent team will be responsible for coming up with the “next big thing” that nobody has thought of as yet, building it and selling it.

 

To be considered, Candidates MUST have extensive experience in the following: Computer Vision, Deep Learning, forecasting, transformers, CNNs. You must also possess strong experience building data models.

Role Responsibilities

Role Responsibilities

Machine Learning Development

• Maintains, as well as furthers, enhances existing machine learning modules for autonomous vehicles.
• Designs and implements new machine learning based approaches based on existing frameworks.
• Keeps up to speed with the state of the art of academic research and technology in the industry.
• Coordinates with engineers at the ICC and in Germany on the development of autonomous driving software.
• Transfers technologies and solutions to Volkswagen Group development divisions.

 

Process Documentation and Representation

• Develops technical specifications and documentation.
• Represents Volkswagen Group in the technical community, such as at conferences.

Qualification requirements

Qualifications

•        6 - 8 years of professional experience post graduate degree PREFERRED 

•        4+ years’ Deep Learning experience post graduate degree PREFERRED

 

Education Requirements:

•        Master’s Degree in Computer Science or equivalent.

  

Education Desired

•        PhD Strongly Preferred

 

Required Skills:

Deep Learning:               

Proficiency in deep learning techniques and frameworks

 

Machine Learning:          

Strong understanding of traditional machine learning algorithms and their applications.

 

Computer Vision:            

Expertise in computer vision, including object detection, image segmentation, and image recognition

 

Natural Language Processing (NLP):

Proficiency in NLP techniques, including sentiment analysis, text generation, and language understanding models. Experience with multimodal language modeling and applications.

 

Neural Network Architectures:

Deep understanding of various neural network architectures such as CNNs, RNNs, and Transformers.

 

Reinforcement Learning:

Familiarity with reinforcement learning algorithms and their applications in AI.

 

Data Preprocessing:

Skills in data cleaning, feature engineering, and data augmentation.

 

Model Training and Tuning:

Experience in training, fine-tuning, and optimizing AI models.

 

Model Deployment:

Knowledge of model deployment