Top Tech Jobs & Startup Jobs in Boston, MA

Design, tape out, and test mixed-signal ASICs interfacing with photonic integrated circuits. Responsibilities include architecture, schematic capture, layout, verification, manufacturing, bring-up, characterization, validation, packaging, and test support for circuits such as ADCs, DACs, TIAs, and transconductance amplifiers. The role also involves cross-functional collaboration with photonics teams and contributing to prototype demonstrations.

Design, tape out, manufacture, bring up, characterize, and validate mixed-signal ASICs interfacing with photonic integrated circuits. Responsibilities include architecture, schematic capture, layout, verification, ADC/DAC and amplifier design, packaging and test support, and cross-functional collaboration on photonic chip integration and prototypes.

The Photonics Design Engineer will design and optimize optical components, lay out circuits, analyze test data, and support prototype development.

As an AI Lead, you will deploy models on prototype hardware, develop algorithms, work with hardware engineers to refine architecture, and support market strategy development.

Lead cross-functional integration of integrated photonics, electronics, hardware, and AI teams to produce system prototypes. Drive system-level models, simulations, requirements, verification planning, program execution, customer-facing technical briefings, and proposal support.

Lead cross-disciplinary integration of photonics, electronics, hardware, and AI teams to deliver working prototypes. Drive system-level models and simulations, translate requirements into scoped work, manage schedules, risks, and resources, and serve as technical/programmatic customer contact while supporting proposals and program reporting.

Lead cross-disciplinary integration of integrated photonics, electronics, hardware, and AI to deliver working prototypes. Drive system-level models, schedules, risk management, customer briefings, and proposal support for government and commercial programs.

The Machine Learning Architect will deploy models on prototype hardware, adapt algorithms for new environments, and collaborate with hardware engineers to define architecture based on practical insights.