Applied AI Engineering Intern
d-Matrix•Santa Clara, CA
•Hybrid
About The Position
As an Applied AI Engineering Intern on the Intelligent Manufacturing Systems team, you will design and implement AI-powered solutions that directly improve manufacturing workflows, yield, and operational throughput. You’ll work hands-on with production data, build and deploy ML models, and collaborate with cross-functional teams spanning hardware engineering, operations, and supply chain. You’ll work at the intersection of LLMs and manufacturing—turning messy real-world data into systems that ship product faster and catch problems earlier.
Requirements
- Pursuing a Master’s or PhD in Computer Science, Electrical Engineering, Industrial Engineering, or a related field
- Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, or scikit-learn)
- Experience with data analysis and visualization (Pandas, NumPy, Matplotlib)
- Familiarity with at least one of: time-series analysis, anomaly detection, optimization, or computer vision
- Familiarity with version control (Git) and Linux-based development workflows
Nice To Haves
- Exposure to manufacturing, semiconductor, or hardware environments is a plus
- Prior internship or project experience in manufacturing analytics, digital twin, or process optimization
- Experience with LLMs/generative AI for structured data or knowledge extraction
- Exposure to statistical process control (SPC) or Six Sigma concepts
Responsibilities
- Build AI agents that diagnose why hardware tests fail—clustering failure signatures, surfacing probable root causes, and helping engineers skip weeks of manual triage
- Design LLM-powered pipelines that ingest unstructured supplier and factory reports and turn them into structured, queryable data visible to the team in real time
- Prototype intelligent document workflows that reconcile financial and procurement records, flagging discrepancies that today require hours of manual cross-checking
- Benchmark multiple LLM backends (cloud and local) across your workloads to find the right cost–quality–latency trade-offs for production deployment
- Collaborate with test, quality, and operations engineers to validate that what the models say actually matches what happens on the floor
Benefits
- Equal Opportunity Employment Policy
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What This Job Offers
Career Level
Intern
Number of Employees
101-250 employees
