Photonics AI Engineer - Component Inverse Design

Job Description

Job Title: Photonics AI Engineer, Component Inverse Design. Location: United States

Responsibilities:

• Own component design from initial physical concepts through to verified building blocks ready for tapeout.
• Apply machine learning, AI-driven inverse design, and AI-assisted optimization to passive and active integrated optical components.
• Execute large-scale simulation campaigns using finite-difference time-domain (FDTD), eigenmode expansion (EME), finite element method (FEM), and mode solvers.
• Evaluate simulation convergence, numerical errors, solver accuracy, and computational costs;
• Develop agentic AI tools and workflows that automate the design process, including launching simulation campaigns, triaging results, and extracting figures of merit.
• Incorporate design-for-manufacturability principles from the beginning to ensure simulated performance translates into fabricated devices.
• Collaborate with layout, fabrication, and test engineers to connect component design with measured wafer data and improve subsequent designs.
• Contribute reusable design, simulation, and optimization infrastructure to the team's codebase.

Qualifications:

• Bachelor's degree in Engineering, Physics, or a related field.
• Strong knowledge of integrated photonic component design, including optical loss mechanisms and their relationship to fabrication processes.
• Experience with electromagnetic simulation techniques, including FDTD, EME, FEM, and mode solvers, with an understanding of the trade-offs between simulation accuracy and computational cost.
• Experience with automated design optimization methods rather than relying solely on manual parameter sweeps.
• Strong Python programming skills, including developing reusable, tested, and version-controlled code;
• Practical experience using modern AI tools in daily engineering work, such as coding agents, large language model (LLM)-driven automation, or agentic pipelines.
• Demonstrated ability to build custom tools and integrate AI capabilities into technical workflows.
• Hands-on experience applying machine learning or AI to a technical problem in any domain.
• Participation in multiple tapeouts involving design, layout, and post-silicon analysis, with components that were fabricated and measured.
• Willingness to rapidly adopt new AI capabilities and adapt engineering workflows accordingly.
• Ability to work effectively in a fast-paced environment with multiple competing priorities.

Preferred:

• Master's degree or PhD in Engineering, Physics, or a related field.
• Experience developing agentic systems or AI tools for technical applications.
• Experience applying machine learning, AI-driven optimization, or inverse design to physical systems.
• Experience designing active photonic components, such as electro-optic modulators or photodiodes.
• Experience with robust design or yield-aware optimization under fabrication process variations.
• Experience executing tapeouts with external photonic foundries;
• Familiarity with quantum photonics or quantum computing hardware.

Technical Environment:

The role primarily uses the following technologies and tools:

• Programming: Python and its scientific computing ecosystem.
• Simulation: Commercial and open-source electromagnetic solvers, including FDTD, EME, FEM, and mode solvers.
• AI and Optimization: AI-assisted engineering tools, coding agents, agentic pipelines, machine learning, and inverse design.
• Infrastructure: Cloud-based high-throughput simulation, GitHub, and Docker.

Proficiency in these technologies and tools is essential.

Benefits and Compensation:

• Base salary: $160,000 to $210,000 USD per year.
• Equity: Eligible for equity compensation.
• Benefits: Employee benefits are available; specific details are not provided in the job description.
• The base salary will depend on the candidate's location, experience, and internal compensation benchmarks.