Torc matches freelance developers to enterprise clients through a vetting and matching engine built on Python, ML models (XGBoost, LightGBM, Random Forest), and real-time data pipelines. The tech stack reveals a data-forward approach: the company is actively building fraud prevention, AML compliance, and real-time suspicious-activity detection systems alongside advanced forecasting models—suggesting Torc's core matching algorithm is backed by continuous performance monitoring and risk assessment. Leadership hiring skews senior (4 of 6 hires), pointing to scaling product and compliance infrastructure rather than team expansion.
Torc is a talent marketplace connecting pre-vetted freelance developers with enterprise clients. Founded in 2021 and based in Boston, the company operates a two-sided platform: developers undergo technical assessments, communication screenings, and coding challenges; enterprises get AI-driven matching, performance insights, and compliance tooling built for procurement, legal, and security teams. The product surface spans developer vetting (matching layer), client dashboards (suspicious activity detection, performance analytics), and backend integrations (payroll, employment data, real-time reconciliation). The company is 11–50 employees, hiring in the United States, with engineering and data teams focused on modernizing legacy systems and operationalizing ML models into planning and forecasting tools.
Python, JavaScript, TypeScript, React, Angular, Node.js, PostgreSQL, Docker, and ML libraries (XGBoost, LightGBM, Random Forest, scikit-learn). Testing via Playwright, Cypress, Jest, and Mocha.
Fraud prevention and AML compliance platforms, real-time data integrations replacing legacy workflows, advanced forecasting models, migration of legacy JSP interfaces to Angular, and customer dashboards for suspicious-activity detection.
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