SentiLink | https://www.sentilink.com/ | Engineering & Data Science | REMOTE, HYBRID, and ONSITE (UNITED STATES ONLY) | Full Time
Primarily hiring in the U.S. for more senior level roles.
We’re hiring across Engineering (Primarily Backend/Data Platform) and Data Science / ML roles. Hiring at Manager/Director level as well as Senior IC levels. We’re language-agnostic and hire for fundamentals.
Across the team you’ll see: • Python, Go, Rust, Scala • PostgreSQL, Snowflake • Kafka, Redis • Terraform, Kubernetes • ML: XGBoost, PyTorch, Feature stores, real-time scoring pipelines
If you love solving complex distributed systems challenges, building customer-facing ML products, or working on detection systems that must be both high-precision and low-latency, you’ll fit right in.
Roles are here: https://jobs.ashbyhq.com/sentilink?utm_source=hacker_news If you have questions, feel free to reach out directly at liz.woodfield at sentilink dot com.
At SentiLink, we stop identity fraud at scale. Our products protect banks, fintechs, marketplaces, and leading financial institutions from synthetic identity fraud, identity theft, and emerging threat. We analyze millions of applications while keeping real users moving fast.
We’ve seen tremendous traction and are growing extremely quickly. Our real-time APIs have helped verify hundreds of millions of identities, starting with financial services and rapidly expanding into new markets. SentiLink is backed by world-class investors including Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin.
SentiLink supports a variety of ways to work, ranging from fully remote to in-office. We operate as a digital-first company with strong collaboration across the U.S. and India. We maintain physical offices in Austin, San Francisco, New York City, Seattle, Los Angeles, and Chicago in the U.S. If you’re located near one of these offices, we would love for you to spend time in the office regularly. Some roles are hybrid or in-office by design.
We’re a small, high-impact team solving one of the most interesting and adversarial problems in fintech: how do you build systems that reliably determine whether a real human is on the other side of a financial transaction?