SentiLink is a San Francisco‑based identity and risk solutions provider founded in 2017 that helps banks, credit unions, and fintechs prevent synthetic fraud, identity theft, and emerging first‑party fraud at the point of account opening and beyond. Leveraging machine learning models and deep expertise from a team of top risk analysts, the company delivers real‑time risk assessment that stops fraud before it occurs. Its technology is widely adopted by major U.S. banks and leading fintech unicorns, enabling secure, compliant onboarding for both institutions and their customers. With $85 million raised from investors such as Andreessen Horowitz, Craft Ventures, and NYCA Partners, SentiLink distinguishes itself by combining advanced analytics with industry‑specific insights to provide a robust, scalable fraud‑prevention platform.
SentiLink is building the future of identity verification in the United States, and we're looking for a Staff Applied ML Scientist to build our core products: models that identify fraudsters and advance our growing suite of products in financial risk. As a Staff Applied ML Scientist, you will work on projects with high visibility and impact that require deep domain understanding, critical thinking, and strong technical abilities.
SentiLink is a San Francisco‑based identity and risk solutions provider founded in 2017 that helps banks, credit unions, and fintechs prevent synthetic fraud, identity theft, and emerging first‑party fraud at the point of account opening and beyond. Leveraging machine learning models and deep expertise from a team of top risk analysts, the company delivers real‑time risk assessment that stops fraud before it occurs. Its technology is widely adopted by major U.S. banks and leading fintech unicorns, enabling secure, compliant onboarding for both institutions and their customers. With $85 million raised from investors such as Andreessen Horowitz, Craft Ventures, and NYCA Partners, SentiLink distinguishes itself by combining advanced analytics with industry‑specific insights to provide a robust, scalable fraud‑prevention platform.