Computer vision AI for dental radiology and pathology detection
Pearl builds AI that reads dental radiographs to flag pathology, sold to dental practices as regulated medical device software. Job posts point to quality-system work (CAPA, NCR, Qualio release sign-off, next-generation QMS tooling) alongside a HubSpot-centered growth push on funnel leakage and lead attribution. Hiring is accelerating and skews senior, with data and design roles leading; Deepgram and Whisper in the stack hint at voice capabilities beyond imaging.
Pearl is a computer vision company applying machine learning to dentistry, with products covering dental radiology, pathology detection, clinical performance and practice management. Its software is positioned as a medical device, which places it under quality-management and regulatory controls. Founded in 2019 and privately held, Pearl employs 51–200 people and is headquartered in West Hollywood, California. Current hiring is concentrated in the United States across data, design, engineering, sales, support and executive functions.
Pearl runs on AWS (Lambda, SQS) with Python, Node.js, TypeScript, React, PostgreSQL, Kafka and Snowflake, plus MLOps tooling, Deepgram and Whisper for speech, and HubSpot for go-to-market.
Pearl is headquartered in West Hollywood, California, and currently hires in the United States.
Pearl is building full-funnel growth analytics, HubSpot lead source attribution, evaluating next-generation QMS tooling, and improving its release sign-off workflow in Qualio.
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Pearl'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 →
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