Lightmatter is leading the revolution in AI data center infrastructure, enabling the next giant leaps in human progress. The company invented the world’s first 3D-stacked photonics engine, Passage™, capable of connecting thousands to millions of processors at the speed of light in extreme-scale data centers for the most advanced AI and HPC workloads.

Lightmatter raised $400 million in its Series D round, reaching a valuation of $4.4 billion. We will continue to accelerate the development of data center photonics and grow every department at Lightmatter!

If you're passionate about tackling complex challenges, making an impact, and being an expert in your craft, join our team of brilliant scientists, engineers, and accomplished industry leaders.

Lightmatter is (re)inventing the future of computing with light!

We’re looking for a Physical Design Flow & Methodology Engineer to help build the tools, flows and automation our physical design teams use to tape out high-performance ASICs and mixed-signal chips. You’ll write production Python, use AI and LLM-based tools to make our flows smarter and faster, and evaluate new EDA capabilities before they reach our implementation teams. You’ll also work with a team of world-class scientists and engineers on design problems that are new to the photonics industry. If you like writing code that makes other engineers more productive, and you want to shape how next-generation chips get built, we’d like to hear from you.

Responsibilities

Develop, maintain and extend our physical design flows from synthesis through signoff (place-and-route, CTS, STA, power integrity, DRC/LVS), mostly in Python and TCL.
Use AI and LLM-based tools to automate flow tasks such as report triage, debug, constraint checking, DRC/LVS analysis, run monitoring and QoR prediction.
Research and prototype new flow capabilities, EDA tool features and methodologies, then turn the successful ones into production flows.
Build tools for regression testing, QoR tracking and dashboards so flow changes are measurable and safe.
Support the implementation teams by debugging flow problems, reproducing tool issues and working with EDA vendors on fixes.
Run blocks through the flow as needed to validate methodology changes, including timing closure, ECOs and DRC/LVS cleanup.
Write clear documentation and train the PD team on new flows and tools.

Qualifications

Bachelor’s in Electrical Engineering, Computer Engineering or Computer Science, plus at least 5 years of related industry experience.
Has contributed to at least one ASIC or mixed-signal chip through tapeout.
Solid understanding of the RTL-to-GDS flow, including synthesis, place-and-route, clock tree synthesis, static timing analysis and physical verification.
Strong Python skills (maintainable, tested, version-controlled code) and working knowledge of TCL and shell scripting.
Practical experience using AI coding assistants or LLM-based tools to automate engineering work, or a strong interest in learning to.
Strong teamwork skills, with the ability to work with PD, RTL, verification, CAD and photonics teams.
Comfortable with ambiguity and changing priorities in a fast-paced research-driven environment.

Preferred Qualifications

Master’s degree with at least 3 years of experience in physical design OR PhD in Electrical Engineering, Computer Engineering or Computer Science
Hands-on experience with Cadence tools (Genus, Innovus, Tempus, Voltus, Pegasus) and/or Siemens Calibre.
Experience developing or owning a production PD flow, or contributing to CAD and methodology infrastructure.
Familiarity with advanced process nodes (FinFET or newer).
Experience with software engineering practices: Git, CI/CD, unit and regression testing, code review.
Experience with LLM APIs, agent frameworks or ML techniques applied to EDA problems.
Experience with layout data formats and tools (GDS/OASIS, LEF/DEF, KLayout scripting).
Interest in silicon photonics and in custom or photonic layout automation.

We offer competitive compensation. The base salary range for this role determined based on location, experience, educational background, and market data.