Applied AI Engineering Intern, d-Matrix

• 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
• Exposure to manufacturing, semiconductor, or hardware environments is a plus
• Familiarity with version control (Git) and Linux-based development workflows

Desirable

• 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
• 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
• Duration: 12 weeks (Summer 2026)