Implantable brain-computer interface with custom neural recording ASICs
Synchron is a Brooklyn-based neurotech company building an implantable brain-computer interface, and its stack shows it designs its own silicon: Cadence, SystemVerilog, UVM and SPICE sit alongside PyTorch and Metaflow for neural signal ML. Senior-weighted, accelerating hiring across engineering, manufacturing and regulatory points to a company moving a new BCI platform toward Class III submissions and production readiness.
Synchron develops implantable neurotechnology, centered on a brain-computer interface intended to address unmet medical needs. Its specialties span neuroprosthesis, neuromodulation, neuromonitoring, machine learning and signal processing. Founded in 2016 and privately held, the company employs 51–200 people and is headquartered in Brooklyn, New York. Hiring is active in the United States and Peru, spanning engineering, manufacturing and regulatory functions. Internal IT runs on Microsoft tools including SharePoint Online, Power Platform and Exchange, with Okta, ServiceNow and SOC 2 controls.
Synchron uses chip design tools like Cadence, SPICE, Verilog, SystemVerilog and UVM, plus Python, C++, PyTorch, TensorFlow and Metaflow for ML, and Microsoft Power Platform, Okta and ServiceNow internally.
Yes. Hiring is accelerating and skews senior, led by engineering roles, with additional openings in manufacturing and regulatory affairs across the United States and Peru.
Synchron is validating a high-channel-count neural recording ASIC, building regulatory strategy for a new BCI platform, and preparing manufacturing through pilot builds, design transfer and process validation.
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Synchron's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.