Applied AI Engineering Intern – Intelligent Manufacturing Systems

Job Description

Applied AI Engineering Intern | d-Matrix

The Tone:
This is a internship at d-Matrix, located in Santa Clara, CA (Hybrid). At d-Matrix, the focus is on unleashing the potential of generative AI to power the transformation of technology, operating at the forefront of software and hardware innovation. This role is crucial for designing and implementing AI-powered solutions that directly improve manufacturing workflows, yield, and operational throughput, turning real-world data into systems that ship product faster and catch problems earlier. The company fosters a culture of respect and collaboration, valuing humility, direct communication, and inclusive teams where differing perspectives lead to better solutions.

The TL;DR
• Role: Internship
• Type: Temporary
• Location: Hybrid, Santa Clara, CA
• Pay: $30–$59 hourly
• Team: Intelligent Manufacturing Systems team
• Mission: Design and implement AI-powered solutions that improve manufacturing workflows, yield, and operational throughput by turning real-world data into systems that ship product faster and catch problems earlier.
• Tech Stack: Python, PyTorch, TensorFlow, scikit-learn, Pandas, NumPy, Matplotlib, Git, Linux

What You’ll Actually Do
• Diagnose Failures: Build AI agents that diagnose why hardware tests fail, clustering failure signatures, surfacing probable root causes, and helping engineers skip weeks of manual triage.
• Process Reports: 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.
• Reconcile Records: Prototype intelligent document workflows that reconcile financial and procurement records, flagging discrepancies that today require hours of manual cross-checking.
• Benchmark LLMs: Benchmark multiple LLM backends (cloud and local) across workloads to find the right cost–quality–latency trade-offs for production deployment.
• Validate Models: Collaborate with test, quality, and operations engineers to validate that what the models say actually matches what happens on the floor.

The Must-Haves
• Background: Masters or PhD student in Computer Science, Electrical Engineering, Industrial Engineering, or a related field.
• Experience: Strong proficiency in Python and machine learning frameworks; experience with data analysis and visualization; familiarity with time-series analysis, anomaly detection, optimization, or computer vision; and familiarity with version control (Git) and Linux-based development workflows.
• Skills: Python programming, machine learning (PyTorch, TensorFlow, scikit-learn), data analysis and visualization (Pandas, NumPy, Matplotlib), version control (Git), Linux environment proficiency, and analytical skills in at least one of time-series analysis, anomaly detection, optimization, or computer vision.
• Bonus: Prior internship or project experience in manufacturing analytics, digital twin, or process optimization; exposure to manufacturing, semiconductor, or hardware environments; experience with LLMs/generative AI for structured data or knowledge extraction; or exposure to statistical process control (SPC) or Six Sigma concepts.