Findem processes unstructured people data through machine learning to extract hiring and workforce signals. The tech stack spans data infrastructure (Snowflake, Kafka, Spark, Airflow, Redshift, Presto) and application layers (TypeScript, React, Node.js, Rails), with recent project activity showing a shift toward AI agents and co-pilot tooling — suggesting the platform is moving beyond traditional data labeling into autonomous decision support. Leadership-tier hiring across product, data, and engineering indicates serious investment in both platform maturity and domain expertise.
Findem is an AI platform for talent acquisition and workforce planning, founded in 2019 and based in Redwood City. The core product ingests unstructured people data and applies machine learning to generate hiring and relationship signals that customers use for recruiting, executive search, and workforce mobility. Active projects span compliance framework implementation, scalable data ingestion pipelines, and AI agent orchestration — reflecting both regulatory maturity and product evolution toward more autonomous capabilities. The company operates at 51–200 employees with hiring across the US, India, and Canada.
Findem's platform uses machine learning to label and structure unstructured people data, generating Success Signals (context about job performance drivers) and Relationship Signals (network and influence mapping) for hiring and workforce planning decisions.
Data layer: Snowflake, PostgreSQL, MongoDB, Kafka, Spark, Hadoop, Airflow, Presto, Redshift. Application: TypeScript, React, Node.js, Python, Rails. Infrastructure: AWS, Docker. Integrations: Salesforce, HubSpot, Marketo.
Yes. Findem has 6 active roles with steady hiring velocity, including senior and principal-level positions in product, data, engineering, ops, and support across the US, India, and Canada.
Findem'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.