Manufacturing Test Engineering Intern

AI Chopping Block, Inc.; Sunnyvale, Northern (CA, KY); Hybrid; USD 39,000 - 44,000; Part time

Job summary

Reality Labs is seeking a Manufacturing Test Engineering Intern in Sunnyvale, CA. You’ll learn the test strategies and build AI tools to automate repetitive steps, while mining data to identify risks in mass production. This 12-week role emphasizes hands-on development and delivering a usable tool by internship end.

Responsibilities include learning end-to-end MTE processes, building AI-enabled automation, and validating results with real test data, with cross-functional collaboration.

Qualifications

• Currently pursuing or having earned a degree in EE, CE, CS or related field.
• Proficiency in Python with scripting and data handling.
• Hands-on experience with AI tools, agents or AI-integrated apps.
• Experience analyzing data to draw conclusions from noisy real-world data.

Responsibilities

• Learn end-to-end MTE workflow from test requirements to tester qualification.
• Design, build, and deploy AI tools and agents for manufacturing tests.
• Build data pipelines and anomaly detection to surface risks early.
• Validate tool output against real test data and iterate based on feedback.
• Present findings, demos, and recommendations to cross-functional teams.

Skills

Python programming

AI tools experience

Data analysis (Pandas/SQL)

Engineering fundamentals

Education

Bachelor's or Master's in Electrical Engineering / Computer Engineering / Computer Science

Tools

Pandas

SQL

Agent frameworks

LLM-based automations

Tool calling architectures

Job description

Reality Labs builds wearable devices that make it easier for people to connect with the ones they love. These devices are manufactured and tested at scale in factories worldwide. The Manufacturing Test Engineering (MTE) team develops the test strategies, test systems, and test coverage that ensure every device we ship is functional and meets product requirements.We are looking for a Manufacturing Test Engineering Intern to learn how manufacturing tests work end-to-end, then help build AI tools and agents that make it more efficient and more reliable. Your work will focus on two outcomes: automating manual, repetitive steps in our workflow, and using data mining to help validate our work by proactively identifying risks of test failures and unusual behaviors. This is a hands‑on build role, with the goal of delivering a working tool the team can use by the end of the internship.This is a 12‑week internship.

Responsibilities

• Learn the MTE role and responsibilities end‑to‑end: supply chain test strategy, test coverage design and validation, tester hardware and software, bring‑up and qualification, and continual improvement in mass production.
• Learn the MTE workflow end‑to‑end, from test requirements through test development to tester qualification, and identify where automation would have the most impact.
• Design, build, and deploy AI tools and agents, including prompt and agent design, tool and data integration, evaluation, and iteration based on engineer feedback.
• Build data pipelines and analysis on manufacturing test data, and develop anomaly detection that surfaces test risks and unusual behaviors early.
• Validate your tool's output against real test data; manufacturing test results are quality‑critical, so accuracy needs to be demonstrated, not assumed.
• Define what success looks like for your project (costs saved, test time reduced, risks caught earlier) and measure your results against it.
• Partner with cross‑functional engineering teams and present your findings, demos, and recommendations to the team.

Minimum qualifications

• Currently has, or is in the process of obtaining, a Bachelors or Masters in Electrical Engineering, Computer Engineering, Computer Science or related field
• Proficiency in a programming language such as Python, including scripting and data handling
• Hands‑on experience using AI tools, and experience building with AI such as agents, LLM‑based automations, or AI‑integrated applications
• Experience with data analysis (Pandas, SQL or equivalent) and drawing conclusions from noisy, real‑world data

Preferred qualifications

• Experience in EE or computer engineering fundamentals, and coursework or project experience in hardware test, instrumentation, measurement, or embedded systems
• Exposure to manufacturing or quality engineering concepts: statistical process control, Cpk, GRR, yield and retest analysis, design of experiments
• Experience with agentic frameworks, retrieval‑augmented generation, tool‑calling architectures, or model evaluation
• Experience with anomaly detection, time‑series analysis, or applied machine learning on engineering or sensor data