UIUC Research Park Intern - ML AI Motor Controls Algorithm

Rivian•Champaign, IL

4d•Onsite

About The Position

Rivian internships are experiences optimized for student candidates. To be eligible, you must be an undergraduate or graduate student in an accredited program during the internship term with an expected graduation date between December 2026 through May 2028. Rivian's Internship Program requires active student enrollment. Information regarding your expected degree completion date is collected solely to verify eligibility and determine your availability for future full-time opportunities. Rivian is an equal opportunity employer and does not use graduation dates to determine the age of applicants or as a basis for discriminatory hiring decisions. If you are not pursuing a degree, please see our full time positions on our Rivian careers site. Note that if your university has specific requirements for internship programs, it is your responsibility to fulfill those requirements. In this role, you will help develop an integrated analytical workflow that leverages Cloud infrastructure and Large Language Models (LLMs), alongside traditional machine learning, to derive actionable insights from complex vehicle data. The project's main focus is architecting an automated, cloud-connected system for high-level data preparation and analysis.

Requirements

Must be currently pursuing a masters or PhD degree at the University of Illinois Urbana Champaign (PhD preferred)
Actively pursuing a degree or one closely related in Computer Science, Computer Engineering, Electrical Engineering, or Physics
Proficiency in AI practices and feeding LLM with data and knowledge base
Strong Programming Fluency: Expertise in Python (including Pandas, NumPy, Scikit-learn) for data analysis and modeling.
Advanced ML Knowledge: Deep theoretical and practical understanding of classification algorithms (e.g., SVM, Decision Trees, ANNs) and model evaluation metrics (e.g., F1-score, precision, recall).
C/C++ Proficiency: Practical experience with C or C++ for embedded systems development and code review.

Nice To Haves

Signal Processing Foundation: Demonstrated coursework or project experience with techniques like FFT, Wavelet Transforms, or Control Theory, preffered.
Understanding of Functional Safety (ISO 26262) principles, preffered.

Responsibilities

Design and implement robust Python-based tools to parse, decode, and extract features from automotive PCAP logs.
Apply advanced signal-processing techniques to transform vehicle data and perform large-scale analysis of corner cases of interest.
Research, develop, and train machine learning models (e.g., Decision Trees, Random Forests, Neural Networks) for prediction, detection, and classification tasks.
Architect the end-to-end workflow that processes raw vehicle logs, performs automated analysis, and prepares data for model training.
Create easy-to-understand dashboards for the general audience, focusing clearly on outcomes to support decision making.