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.
- Comprehensive Health Care Plan (Medical, Dental & Vision)
- Retirement Savings Matching Program
- Life Insurance (Basic, Voluntary & AD&D)
- Generous Time Off (Vacation, Sick & Public Holidays)
- Paid Family Leave
- Short Term & Long Term Disability
- Training & Development
- Commuter Benefits
- Flexible, hybrid workplace model
- Equity grants (applicable to full-time employees)
Benefits eligibility may vary depending on your employment status and location. Lightmatter recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law.
Export Control
Candidates should have capacity to comply with the federally mandated requirements of U.S. export control laws.
Skills Required
- Bachelor's degree in Electrical Engineering, Computer Engineering, or Computer Science
- At least 5 years of related industry experience
- Contribution 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, including maintainable, tested, and version-controlled code
- Working knowledge of TCL and shell scripting
- Practical experience using AI coding assistants or LLM-based tools to automate engineering work, or strong interest in learning to use them
- Strong teamwork skills and ability to work with PD, RTL, verification, CAD, and photonics teams
- Comfort working with ambiguity and changing priorities in a fast-paced research-driven environment
- Master's degree with at least 3 years of physical design experience
- PhD in Electrical Engineering, Computer Engineering, or Computer Science
- Hands-on experience with Cadence Genus, Innovus, Tempus, Voltus, Pegasus, and/or Siemens Calibre
- Experience developing or owning a production physical design flow or contributing to CAD and methodology infrastructure
- Familiarity with advanced process nodes, including FinFET or newer
- Experience with Git, CI/CD, unit and regression testing, and code review
- Experience with LLM APIs, agent frameworks, or machine learning techniques applied to EDA problems
- Experience with GDS/OASIS, LEF/DEF, and KLayout scripting
- Interest in silicon photonics and custom or photonic layout automation
Lightmatter Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Lightmatter and has not been reviewed or approved by Lightmatter.
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Fair & Transparent Compensation — Pay is considered competitive for specialized photonics and AI‑hardware roles, with posted ranges and third‑party totals aligning to strong market levels. Compensation for niche roles is framed as consistent with top‑tier hardware/photonics expectations.
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Healthcare Strength — Healthcare is described as comprehensive, covering medical, dental, and vision in current postings. Additional protections like life insurance and disability are also called out.
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Equity Value & Accessibility — Equity grants for full‑time employees are commonly included alongside base pay. Some postings also mention performance‑based equity components.
Lightmatter Insights
What We Do
Lightmatter is a startup photonics company that has pioneered a new paradigm in processor chip architecture that uses photons instead of electrons. Now the leader in the photonic AI compute “space race,” this new class of high performance semiconductors is the foundation of Lightmatter’s mission to enable the growth of computing, reduce harmful emissions associated with this growth, and democratize computing for all. Lightmatter's technology accelerates critical operations in deep neural networks, using an array of programmable photonic elements fabricated alongside transistors in conventional computer chip fabrication processes. Our close-knit team of engineers has created an integrated photonics chip and ultra-high performance interconnect that is faster, more efficient, and cooler than anything else on earth (or anything ever experienced before) to power the next giant leaps in human progress.
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