Manufacturing Test Engineering Intern, AI

Meta; Sunnyvale (CA); On-site; USD 28,000 - 48,000; Full time

Job summary

Meta Reality Labs is seeking a Manufacturing Test Engineering Intern in Sunnyvale, CA, to learn end-to-end manufacturing tests and help build AI tools that increase efficiency and reliability. The internship spans 12 weeks and focuses on automating repetitive steps and using data mining to predict test failures.

You will collaborate with cross-functional teams, design AI-driven workflows, and demonstrate impact through measurable improvements in test time and risk mitigation.

Qualifications

• Pursuing or completed degree in EE/CE/CS or related field.
• Proficiency in Python programming and data handling.
• Hands-on experience with AI tools and AI-based automations.

Responsibilities

• Learn MTE responsibilities end-to-end, including test coverage design and validation.
• Design, build, and deploy AI tools and agents for automation.
• Build data pipelines and perform anomaly detection on manufacturing test data.
• Validate tool outputs against real test data and measure success against defined metrics.

Skills

Python

Pandas

SQL

AI tools

Education

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

Tools

Git

Job description

About

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 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