Applied AI Engineering Intern

At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration.

We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution.  Ready to come find your playground? Together, we can help shape the endless possibilities of AI. 

Intelligent Manufacturing Systems

At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration. We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions.

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

• 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

• 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