Meta（Meta Platforms）岗位：Optical Engineering AI and Data Analytics Intern Responsibilities
Upstream Data Ingestion:Build automated ingestion paths and pipeline schemas to integrate previously offline lab and bench measurement data.
Tool Calibration AI Agent:Develop an AI agent and system of record to track instrument certification, trigger automated maintenance alerts, and coordinate service scheduling.
Production Test Analytics:Implement automated analytics layers for measurement system analysis (Gauge R&R), station-to-station correlation, and drift detection across large-scale production test data.
Minimum Qualifications
Currently pursuing a BS, MS, or PhD in Computer Science, Data Science, Statistics, Electrical Engineering, or a related field
Proficiency in Python or TypeScript/JavaScript, and comfort learning the other
Working knowledge of SQL and experience querying a relational or columnar data store
Foundation in statistics and data modeling: distributions, hypothesis testing, regression, and fitting and judging a model against held-out data
Experience building something other people used, such as a tool, pipeline, dashboard, model or library. Coursework counts if the artifact was real
Able to explain a technical result, and how confident you are in it, to someone who does not share your background
Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
Intent to return to full-time degree program after completion of the internship
Preferred Qualifications
Predictive modeling and time-series analysis: forecasting, trend and change-point detection, anomaly or drift detection, and back-testing a model before trusting it
Statistical process control and measurement systems analysis: Cpk, control charts, Gauge R&R, station-to-station correlation
Data modeling and pipelines: designing warehouse schemas, and distributed processing (Spark, Presto, Hive or equivalent) with ETL / pipeline orchestration
Full-stack web development for data products: React / Next.js with TypeScript, server-side APIs (REST or GraphQL), and scheduled jobs
Building with LLMs:agents, tool use, retrieval, or evaluation
Testing and validating data products: automated checks that the numbers shown match the source data, and catching silent regressions
Exposure to manufacturing, test, or instrumentation data and its usual problems: inconsistent station naming, missing metadata, silent schema drift
An interest in optics or imaging. Useful, but genuinely not required
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