Apprentice Machine Learning Testing Jobs in Minneapolis, MN

OVERVIEW
Job Title: Minnesota Semiconductor AI Hub- Manufacturing AI Intern
Location:St. Paul
Pay Rate: $25/per hour
Hours: Up to 20 hours a week.
The Minnesota Semiconductor AI Hub is a collaborative initiative between the University of St. Thomas College of Engineering and local semiconductor manufacturing companies, including Seagate, SkyWater Technology, and Polar Semiconductor. The Hub focuses on developing data-driven and AI-powered solutions to shared manufacturing challenges with the goal of advancing capabilities that benefit the broader Minnesota semiconductor industry.
The Hub is seeking one motivated student intern to help drive rollout of AI inside production and enterprise systems. You will work alongside Hub's industry partner SkyWater and Electrical & Computer Engineering faculty on applied projects with direct relevance to partner company operations. This is a year-long role for rising and recent graduates who are passionate about machine learning, semiconductor manufacturing, industrial digitization, and data-driven problem solving. Candidates will contribute to model development, generative AI augmentation, data pipelines, and evaluation frameworks in an environment at the confluence of traditional manufacturing processing and next-gen digital transformation.
U.S. Person Required:
The MN AI Hub partner SkyWater Technology Foundry, Inc. is subject to the International Traffic in Arms Regulations (ITAR). All accepted applications must be U.S. Persons as defined by ITAR. ITAR defines a U.S. Person as U.S. citizen, U.S. Permanent Resident, Political Asylee, or Refugee.
Expected Work:
The intern will support fab-level AI initiatives focused on improving manufacturing efficiency, engineering knowledge access, and tool uptime. Expected work includes:
- Building and testing machine learning models for tool maintenance prediction, part replacement forecasting, wafer scheduling, and downtime reduction.
- Evaluating generative AI context retrieval strategies to capture BKMs, engineering knowledge, and corporate documentation for use in production and enterprise systems.
- Supporting data pipeline development, feature engineering, model evaluation, and monitoring needed for scalable AI deployment.
- Assessing SME-supported modeling and practices based on data availability, problem complexity, dimensionality, and expected sample requirements.
- You may be required to travel to and work closely with collaborators at the partner site.
Responsibilities:
- Design, develop, test, and deploy machine learning models and agentic systems.
- Work with and create large-scale datasets to train, evaluate, and improve models.
- Analyze model performance and identify opportunities for optimization.
- Establish defect trend monitoring within each fab module to drive improvement in every area of manufacturing.
- Review & respond to fab defect trends using statistical process control principles.
- Stay current with developments in machine learning, deep learning, and AI systems.
- Work with integration & engineering teams to assist in fab digitization work.
- Build and leverage expertise in creative problem solving.
- Organize and present data findings and model results to engineering modules and group leaders, proposing action based on trends and signals.
- Utilize excellent communication skills to deliver information effectively internally and externally, with key stakeholders and sponsors.
QUALIFICATIONS
Required Qualifications:
- Rising MS graduate in Software Engineering, Data Science, Artificial Intelligence, Electrical & Computer Engineering
- Experience working with large datasets, SQL, data pipelines, or cloud-based tools.
- Experience with data structures, algorithms, statistics, and software engineering fundamentals.
- Experience with programming in Python, Java, C++, or a similar language.
- Foundational knowledge of machine learning concepts such as supervised learning, natural language processing, model evaluation, optimization, and neural networks.
- Understanding of generative AI concepts such as retrieval augmented generation, supervised fine-tuning, in-context learning, harness engineering, and multi-agent systems.
- Passion for building responsible, scalable, and user-focused AI systems.
- Strong organization and communication skills to manage tasks to effectively execute and commit deliverables.
- Excellent troubleshooting skills.
- Ability to work collaboratively in a fast-paced technical environment.
- Understanding of semiconductor processing, fab operations, equipment is preferred
- Experience in data engineering is preferred.
- Experience in 3D modelling and NVIDIA Omniverse is a plus.
- Experience in Quantum Programming is a plus.
- Fundamental understanding of analytic techniques is a plus.
SPECIAL INSTRUCTIONS FOR CANDIDATES
On the Application please clearly explain - how you meet the required qualifications.