QuEra Computing
Photonics Engineer
$128K–$208K
Boston, MA, USA +1 · Full-time · Posted Aug 21, 2026
- Location
- Boston, MA, USA
Boston, Massachusetts, United States - Experience
- 4+ years with photonic integrated circuits (PICs), as an alternative to the stated degree requirement.
- Education
- Master's or PhD in Electrical Engineering, Physics, or a related field, or equivalent experience (4+ years with PICs).
About the job
Develop photonic components and AI-driven design pipelines to accelerate photonic chip development for neutral-atom quantum computers.
What you'll do
- Lead tapeouts of photonic and optoelectronic components and circuits; build AI-driven component development pipelines, initially focused on layout.
- Collaborate with foundry partners through design, fabrication, and testing cycles, using PDK constraints and feedback to improve devices.
- Contribute to photonic design, simulation, layout, testing, and system-level experiments, and integrate components into quantum computing systems.
What they ask for
- Master's or PhD in Electrical Engineering, Physics, or a related field, or 4+ years of experience with photonic integrated circuits (PICs).
- Practical understanding and experience across PIC workflows, from simulation and layout to experimentation and control.
- Strong Python skills and a demonstrated record of engineering-work automation, including with AI tools.
- Technical impact evidenced by publications, patents, or shipped products.
Nice to have
- Experience with PIC simulation or layout tools, applied ML or AI for device design or layout optimization, or a foundry PDK and its design-rule constraints.
- Experience with optical systems for atomic physics, quantum or other physics-heavy hardware, or photonic design automation, large-scale layout, high-volume testing, or systems control.
Pay
$128,000 - $208,000 USD base salary
Tools
Python, Lumerical, Tidy3D, gdsfactory, KLayout, gdspy, IPKISS
AI focus
Own and build AI-driven component development pipelines, initially focused on layout; use or apply AI tools and potentially ML/AI methods such as inverse design, Bayesian optimization, and LLM-assisted design or layout workflows.
Model-written summary · the original posting is authoritative