Photonics AI Engineer — Fabrication-Aware Design (Equity)

Xanadu; Santa Clara (CA); On-site; USD 160,000 - 210,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

Xanadu is hiring a Photonics AI Engineer to own fabrication-aware design on a fast-paced team. You will translate process constraints into design language, calibrate models with wafer data, and build AI tooling to turn metrology into design changes.

Proficiency with Python, GitHub, Docker, and cloud compute is essential. You’ll work with ML/AI specialists to drive process-design co-optimization and lead failure analysis while collaborating across design and fabrication teams in a dynamic,

Qualifications

• Fluency with modern AI tooling and agentic workflows.
• Experience in multiple tapeouts with design decisions or DOE input.
• Strong Python skills and data-driven mindset.
• Excellent collaboration with ML/AI teams and design/process audiences.

Responsibilities

• Bridge fabrication and design translating process constraints into design language.
• Own fabrication-aware layer of design methodology including layer stacks and bias targets.
• Calibrate process models against wafer data for CD, edge bias, and lithographic rounding.
• Define tolerance and yield budgets and assess design robustness.
• Contribute test structures to fabrication experiments and DOEs.
• Turn metrology and yield data into design changes with AI tooling.
• Lead failure analysis distinguishing design error from process excursion.

Skills

AI tooling

Tapeouts

Python

Communication

Education

BSc in Physics

MSc/PhD in Physics

Tools

GitHub

Docker

Job description

Xanadu is hiring a Photonics AI Engineer to own fabrication-aware design on a fast-paced team. You will translate process constraints into design language, calibrate models with wafer data, and build AI tooling to turn metrology into design changes.

Proficiency with Python, GitHub, Docker, and cloud compute is essential. You’ll work with ML/AI specialists to drive process-design co-optimization and lead failure analysis while collaborating across design and fabrication teams in a dynamic,