AI Data Science / Software Engineering PhD Intern New 2027

Intel•Us, CA

6d•Onsite

About The Position

As an AI Software Engineering PhD Intern, you will contribute to designing, developing, and optimizing AI software solutions across areas including computer vision, machine learning, and deep learning. You will implement and tune AI models for performance and accuracy, explore applied research opportunities, and work on hardware-software integration to enable scalable, efficient AI solutions. The role supports Intel Foundry initiatives to advance manufacturing and technology capabilities and deliver transformative products and solutions to customers. You will design and implement data-driven solutions for tool capacity management, risk modeling, and defect inspection. The work includes building cloud-based ETL pipelines and predictive models, publishing reports and dashboards, and building an MCP server to connect manufacturing data and analytical models to Intel’s in-house generative AI and LLM service so engineers can query manufacturing data conversationally.

Requirements

Must be pursuing a PhD in Computer Science, Data Science, AI, Machine Learning, Statistics, or a related discipline.
At least 6 months of programming experience.
At least 3 months of experience with Git, Docker, Helm, Kubernetes, and MCP server.
Proficiency in Python programming and machine learning techniques.

Nice To Haves

Experience with data analysis and visualization.
Knowledge of statistical analysis and modeling techniques.
Strong understanding of algorithms, data structures, and database fundamentals.

Responsibilities

Design and implement data-driven solutions to optimize tool capacity management, risk modeling, and defect inspection strategies.
Build cloud-based ETL pipelines.
Develop predictive models.
Publish reports and dashboards that deliver actionable insights.
Build an MCP server connecting manufacturing data and analytical models to the in-house generative AI and LLM service.
Design, develop, and optimize AI software solutions in areas such as computer vision, machine learning, and deep learning.
Implement and tune AI models for performance and accuracy.
Explore applied research opportunities.
Contribute to hardware-software integration for scalable and efficient AI solutions.