Test and Manufacturing Intern

Calix

1d•Remote

About The Position

This is a full time, remote Summer Internship that can be based anywhere in the United States or Canada. Calix is looking for a Summer intern to join our Test and Manufacturing Engineering team. In this role, you will be part of a unique and award-winning internship program within the company. The program provides the opportunity to learn new skills through training and on the job learning. The expected duration is 90 days. Calix is looking for a Test and Manufacturing Engineering Intern to join our Control Tower initiative a cross-sites manufacturing analytics program. The intern will assist with testing activities, data analysis, and reporting to support product quality and manufacturing performance. This is an exciting opportunity for any aspiring Engineering student to come into a high growth, fast paced environment and get real hands-on experience with our Test and Manufacturing Engineering team.

Requirements

Currently pursuing a Bachelor's or Master's degree in Engineering, Computer Science, Data Science, Industrial Engineering, or a related field.
Familiarity with data analysis concepts and working with structured/unstructured datasets.
Experience in a manufacturing role or project requiring analytical problem solving, critical thinking, and analysis.
Basic Familiarity with manufacturing or test data (MES, test logs, quality systems)
Hands-on experience with one or more of the following: Python, SQL, Microsoft Office
Ability to communicate clearly, Strong attention to detail and work with multiple teams (engineering + data + Supplier)
Ability to work independently in a remote environment with a high tolerance for ambiguity and fast-changing environments.
Interest in Industry 4.0, manufacturing systems, automation, data analytics, or digital manufacturing concepts
Ability to work for the complete summer break (May - August or June - September).

Nice To Haves

Experience with JIRA, AI or test management tools
Exposure to Power BI or similar visualization tools, data tool Snowflake.
Basic exposure AL, ML exposure (classification, clustering, anomaly detection)
Coursework or personal interest in smart manufacturing, Industry 4.0, or industrial data systems
Experience working with surveys, spreadsheets, or basic data analysis for school projects
Exposure to manufacturing environments through internships, labs, or class projects

Responsibilities

Collaborate with engineering stakeholders and internal /external data engineering team to help build and improve analytics solutions.
Support data collection, ingestion, and integration from manufacturing environments (test data, production metrics, quality indicators, yield, etc.)
Perform data validation and quality checks across manufacturing, test, and quality datasets
Analyze test and manufacturing data and help create or refine dashboards/reports to track KPIs (yield, throughput, FPY, rework, defect trends)
Assist with root-cause trend analysis and communicate findings through clear visuals and summaries
Support AI/ML-driven analytics, including anomaly detection, outlier analysis, or predictive indicators
Document processes, data flows, and standard operating procedures (SOPs)
Assist senior engineers in gathering and organizing information related to factory digitalization and Industry 4.0 practices
Help define assessment criteria, scoring models, and maturity levels to consistently evaluate suppliers’ Industry 4.0 capabilities and readiness.
Analyze supplier survey responses and factory data to identify gaps, strengths, and improvement opportunities across suppliers and manufacturing sites.
Translate survey results into clear dashboards, visual summaries, and executive‑ready insights that can be used by engineering, supply chain, and leadership teams.
Assist in documenting best practices and emerging Industry 4.0 use cases observed across suppliers to help inform future investments and supplier engagement strategies.
Contribute to continuous improvement efforts for the Manufacturing Control Tower by helping identify opportunities to improve factory data visibility.