Sr Technical Product Manager, Bot & AI Automation Intelligence

About this role

Fingerprint empowers developers to stop online fraud at the source.

We work on turning radical new ideas in the fraud detection space into reality. Our products are developer-focused and our clients range from solo developers to publicly traded companies. We are a globally dispersed, 100% remote company with a strong open-source focus. Our flagship open-source project is FingerprintJS (27K stars on GitHub).

We have raised $77M and are backed by Craft Ventures (previously invested in Tesla, Facebook, Airbnb ), Nexus Venture Partners (previously invested in Postman, Apollo.io, MinIO, Druva) and Uncorrelated Ventures (previously invested in Redis, Rollbar & Gradle).

We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Fingerprint recruiting email communications will always come from the @fingerprint.com domain. Any outreach claiming to be from Fingerprint via other sources should be ignored.

We’re looking for a Senior Technical Product Manager to own the strategy and execution of Fingerprint’s Bot Detection offering in an increasingly AI-automated world — from defining what we detect (and how we classify intent), to shipping customer-facing product experiences, to driving adoption and commercial outcomes.

Location / level: Remote-friendly (Americas → Central European time zones preferred).

What you’ll own

1) Bot Detection product strategy & roadmap

• Set mission, vision, and strategy for Bot Detection as a distinct product line (including how it fits alongside Identification and Smart Signals).

• Own the roadmap across detection capabilities, taxonomy/identity models, customer-facing UX, and go-to-market readiness.

• Define how we evolve from “bot detection” toward automation + intent intelligence (covering AI assistants, agentic traffic, direct-to-API automation, and emerging adversarial techniques).

2) Detection capabilities & intelligence (the “what we detect” layer)

• Drive the plan for expanding and improving detection coverage (e.g., anti-detect browsers, network and IP intelligence, AI assistant detection, agentic automation patterns).

• Partner with Engineering and Data Science to define evaluation methodology, quality targets, and iteration loops (false positives/false negatives, coverage, robustness).

• Own the detection taxonomy and classification semantics (e.g., good / bad / unknown automation, spoofed identity patterns, verified/signed bots where relevant).

• Translate competitive and threat landscape trends into prioritized detection investments.

3) “Beyond JS” / edge & server-side automa