Internship – Data Management

QuEra Computing, Inc.Boston, MA
Onsite

About The Position

The Photonics team at QuEra pioneers cutting-edge photonic chips for integration into neutral-atom quantum computers. QuEra Computing Inc. seeks a Photonics Data Engineer Intern to develop an AI-powered data infrastructure that supports our design, fabrication, and testing workflows. You'll develop tools that transform raw measurement and metrology data into actionable insights, directly informing how we design better photonic chips faster for large-scale neutral atom quantum computers.

Requirements

  • Pursuing or recently completed a degree in computer science, physics, mathematics, or a related field.
  • Strong programming skills in Python and AI tools, including experience with data manipulation libraries (e.g., PyTorch, NumPy, SciPy, Pandas) and workflow automation.
  • Familiarity with data storage formats, database design principles, data analysis, and visualization tools.
  • Excellent problem-solving and analytical skills.
  • Strong communication and teamwork skills.
  • Strong evidence of independent work.
  • While a solid foundation in quantum physics is not necessary, a strong background in computer science and mathematics is required.
  • On-site internship. There is no relocation offered for this role.

Responsibilities

  • AI-powered Data Infrastructure: Develop an AI-powered data management infrastructure for photonic integrated circuit (PIC) design, fabrication, and testing workflows.
  • Automation and Tooling: Write Python functions and utilities to automate data ingestion, cleaning, and organization across multiple data sources.
  • Performance Analysis Pipelines: Build analysis pipelines to extract key device performance metrics (e.g., insertion loss, extinction ratio, bandwidth, yield). Analyze fabrication metrology datasets from multiple foundries and correlate findings with device performance to identify process-performance relationships.
  • Cross-functional Collaboration: Work with other photonics engineers to define data schemas and best practices for reproducible design and testing workflows.
  • Documentation: Document data infrastructure and analysis workflows for long-term team use.
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