2026 Internship Project: Josephson Junction Yield & Data Systems at Rigetti

Team: JJ Modules

Rigetti Computing is developing cloud-deployable quantum computers to solve humanity’s most pressing and valuable problems. We are a highly diverse and interdisciplinary team of scientists and engineers working at the cutting edge of technology in a mission-driven entrepreneurial environment. We believe that bringing teams from various cultures, educational backgrounds, and technical disciplines helps them to see things from more points of view and to make great products that change the world for the better.

Josephson Junction (JJ) yield is a critical driver of chip selection and scalability for larger quantum systems. As we scale to higher qubit-count architectures, robust tracking of JJ targeting, spread, opens/shorts, and wafer-level yield is essential to ensure predictable performance and informed calibration decisions. As a Josephson Junction Yield & Data Systems Intern, you will work with the JJ process integration, E-beam lithography (EBL) module, and Internal Software (ISW) teams to establish a structured internal platform for JJ yield analytics. You will evaluate and improve existing internal tools for JJ yield tracking by designing and implementing dashboards for tracking JJ resistance targeting, wafer-level spread, and key SPC metrics. This tool should enable data-driven JJ size calibration inputs for future wafers.

You will work in a fast-paced environment and interact with a range of engineers and scientists in process, integration and design.

Key Responsibilities

Design and implement a structured dashboard for tracking JJ yield metrics, including JJ resistance targeting, wafer-level spread, opens/shorts rate, and overall die yield.

Integrate room-temperature probe data, MES outputs, and relevant fabrication metadata into a unified analytics framework.

Evaluate existing internal tools and determine whether upgrades are sufficient or if a new system should be developed.

Enable transparent, shareable tracking of JJ yield data that is currently semi-automated.

Develop statistical process control (SPC) visualizations for JJ resistance targeting and spread.

Establish automated baseline tracking and drift detection.

Build input/output capability to support JJ size calibration workflows for future wafers.

Support data-driven decisions for safer targeting windows and improved yield consistency.

Additional Responsibilities

Collaborate with ISW and process engineers to improve automation and analytics workflows.

Support migration or enhancement of analytical toolsets within the JJ module.

Contribute to improving usability and accessibility of internal yield metrics tools.

Work closely with JJ integration, EBL, and process engineering teams.

Present findings and recommendations in team meetings.

Document workflows, assumptions, and analytical methods.

Maintain organized records of processed data and derived insights.

Contribute to best practices for long-term JJ yield tracking and dashboard sustainability.

Required Qualifications

Currently enrolled in a Bachelor’s or Master’s program in Physics, Electrical Engineering, Materials Science, Data Science, or related field.

Strong Python skills (pandas, matplotlib/plotly, scientific computing).

Experience with data analysis and statistical reasoning.

Comfort working with structured data (CSV, SQL-like formats, APIs).

Clear written and verbal communication skills.

Preferred Qualifications

Familiarity with semiconductor fabrication, lithography, or device physics.

Understanding of statistical process control (SPC).

Experience building dashboards (Dash, Streamlit, Plotly, etc.).

Exposure to superconducting devices, qubits, or cryogenic measurement environments.

Experience working with production or manufacturing data systems.

2026 Internship Project: Josephson Junction Yield & Data Systems

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