Job Description

"Vorsprung durch Technik" or, "Progress through Technology" is our company ethos and is the core belief that drives the development of every Audi vehicle. It inspires the innovative solutions, revolutionary engineering and progressive design that have become synonymous with the Audi name. And it reminds us to challenge ourselves, rewrite the rules from time to time and always fuel our pioneering spirit. The spirit that sets us apart on the racetrack and the open road. In our relentless pursuit to become the number one premium car brand in America, we need employees who share in this spirit of challenging the status quo in everything that we do. Role Summary: As an ADAS Tooling Development Software Fellow, you will join an interdisciplinary team focused on advancing autonomous driving technologies through AI-powered tooling. Over the course of the fellowship, you’ll contribute to the development, evaluation, and deployment of machine learning models that analyze real-world driving scenarios from video data. Your work will involve hands-on data processing, model training, performance analysis, and integration into production pipelines, with opportunities to validate your models through in-vehicle testing. This role is ideal for students eager to apply their research skills in a fast-paced, collaborative environment tackling real-world challenges in autonomy. Role Responsibilities: Collection and processing of data (20%) Participate in-vehicle test drives to collect data Process the corresponding data Designing, implementing, testing, deploying and monitoring AI models (70%) Design artificial intelligence models to perform the desired tasks Implement the models in python using Deep learning packages. Test the model performance to make it complacence with the desired KPIs. Deploy the model using containerization and orchestrations tools. Monitor the model’s performance to make sure it maintains the desired performance Reporting of results (10%) Create reports illustrating the model’s performance. Extract key insight of the model performance and the underlying data structure Communicate advanced ideas with the team. Qualifications: Required Education: Applicants must be rising seniors in a Bachelor's or Master's program, currently enrolled Ph.D. students, or recent graduates who completed their degree within the past six months. Electrical Engineering, Mechanical Engineering, Computer Science, or related field preferred. GPA of 3.0 or higher Required Skills: Proficiency with Python (3+ years) and the Python data stack (Numpy, Scipy, Pandas, Scikit-learn) and/or SQL Good understanding in AI, Deep Learning and Machine Learning including but not limited to CNN/RNN architectures, Image and semantic segmentation, video processing Good understanding of statistical modeling, machine learning, deep learning, or data analytics concepts. Experience with one or more Deep Learning packages including but not limited to Pytorch and TensorFlow. Excellent skills in data manipulation, preprocessing and feature engineering. Experience with predictive modeling (Regression, Classification, Clustering, Dimensionality Reduction) Experience with deploying machine learning models in production using containerization and orchestrations tools like Docker Experience with SQL Must have a valid Driving License Desired Skills: Knowledge of the following tools – Codebeamer, JIRA, Confluence, bitbucket, git, conan, ADTF, ROS 2, VMware/virtualbox, docker, Ubuntu, QNX In San Jose, California, the hourly rates for this role are based on level of education: Undergraduate (Junior or Senior): $35/hour Graduate (Master's): $38/hour PhD: $42/hour We are unable to consider OPT/CPT/International candidates. Volkswagen Group of America is an Equal Opportunity Employer. We welcome and encourage applicants from all backgrounds, and do not discriminate based on race, sex, age, disability, sexual orientation, national origin, religion, color, gender identity/expression, marital status, veteran status, or any other characteristics protected by applicable laws. #J-18808-Ljbffr Audi USA

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