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

  • Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Physics, Mathematics, or a related discipline.
  • Strong proficiency in Python programming, with hands-on experience using AI and data science libraries including PyTorch, NumPy, SciPy, and Pandas, and familiarity with workflow automation tools.
  • Working knowledge of data storage formats, database design principles, and data analysis and visualization frameworks.
  • Proven track record of leveraging Python and AI tools to tackle challenging problems in science and physics.
  • Strong analytical mindset with exceptional problem-solving capabilities.
  • Excellent interpersonal and communication skills, with the ability to collaborate effectively across multidisciplinary teams.
  • Clear evidence of self-motivation and the ability to drive independent projects to completion.
  • A solid understanding of computer science and mathematics in the context of physical simulation and experimental analysis is required.

Nice To Haves

  • Deep background in quantum physics is not a prerequisite.

Responsibilities

  • Develop an AI-powered data management infrastructure for photonic integrated circuit (PIC) design, fabrication, and testing workflows.
  • Write Python functions and utilities to automate data ingestion, cleaning, and organization across multiple data sources.
  • 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.
  • Work with other photonics engineers to define data schemas and best practices for reproducible design and testing workflows.
  • Document data infrastructure and analysis workflows for long-term team use.
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