Drimva

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

First seen Oct 29, 2026 · Last verified Nov 4, 2026 (UTC)