Applied AI Engineering Intern

d-Matrix

Posted 2 months ago

Internship

Santa Clara, California

Hybrid

Smart Summary

Responsibilities

You will design and implement AI-powered solutions to improve manufacturing workflows, yield, and operational throughput. This involves building AI agents for failure diagnosis and creating LLM-powered pipelines to structure unstructured factory data.

Qualifications

You are pursuing a Master's or PhD in a relevant technical field and possess strong Python programming skills with experience in ML frameworks like PyTorch or TensorFlow. You also have experience with data analysis and visualization tools such as Pandas and NumPy.

Must Have Skills for ATS

Python

PyTorch

TensorFlow

scikit-learn

Pandas

NumPy

Matplotlib

time-series analysis

anomaly detection

optimization

computer vision

Git

Linux

Job Description

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. 

Applied AI Engineering Intern

Intelligent Manufacturing Systems

About d-Matrix

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.

About the Role

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.

Responsibilities

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

Qualifications

• 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

Nice to Have

• 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

Details

Location: Santa Clara, CA (Hybrid)

Compensation: $30–$59/hr (commensurate with experience and education)

Duration: 12 weeks (Summer 2026)

d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn togeth

Equal Opportunity Employment Policy

d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.

d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.

d-Matrix

To make AI inference commercially viable, d-Matrix has built a new computing platform from the ground up: Corsair™, the world’s most efficient compute solution for AI inference at datacenter scale. We are redefining Performance and Efficiency for AI Inference at scale.
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