Engineering Manager, Automation Intelligence

Fingerprint empowers enterprises to detect and stop online fraud with the world’s most accurate device intelligence. We lead our industry with bleeding-edge identification capabilities and work on turning new ideas and discoveries in the fraud detection space into reality. Our customers range from innovative startups to leading enterprise companies, including Plaid, Dropbox, and Booking.com. Fingerprint is a globally dispersed, 100% remote company. We were named on on the 2026 Forbes Best Startup Employers list and ranked #803 on the 2026 Inc. 5000 list of America’s fastest-growing private companies. We have raised $77M and are backed by Craft Ventures (Tesla, Facebook, Airbnb ), Nexus Venture Partners ( Postman, Apollo.io, MinIO, Druva) and Uncorrelated Ventures ( Redis, Rollbar, Gradle). The Role in Context Fingerprint's device intelligence platform processes billions of identification events for companies that need to know who — or what — is on the other end of a request. Increasingly, the answer is not a person. AI agents, AI assistants, crawlers, headless browsers, and malicious automation now make up a large and fast-growing share of internet traffic, and telling them apart from humans — and from each other — is becoming one of the most important problems our customers ask us to solve. We are standing up a dedicated Automation Intelligence engineering team to own that problem end to end: detection in the browser, detection at the edge, the classification taxonomy that tells customers what kind of automation they're seeing and how confident we are, and the API that customers and their agents call. We're looking for an Engineering Manager to lead this team. You'll inherit a small, senior group (three engineers to start, growing to four or five) and a clear mandate. You report directly to the VP of Engineering. Your Mission Ship the product — Take the automation intelligence product through Beta to GA, and then to a standalone commercial launch. You own engineering readiness: scope, estimates, service reliability, operating limits, API and MCP coverage, and the evidence that it's ready Make detection accurate and keep it that way — Build the team's quality loop: a test corpus of known bots, agents, and automation frameworks; continuous false-positive and false-negative testing; a regression test for every confirmed gap; and coverage benchmarked against the best in the industry. Detection quality is the product Build a team that runs itself — Set the team up so adding a detection, shipping a change, or running a staged rollout doesn't depend on another team's calendar. Grow the engineers, hire the next seats, and create an environment where senior people do their best work Lead the AI-native engineering model — AI agents write the code by default and engineers decide what gets built and whether it's right. You'll operate that model, measure it honestly, and produce the playbook the rest of engineering adopts What You'll Do