Sr Technical Product Manager, Bot & AI Automation Intelligence

Fingerprint empowers developers to stop online fraud at the source through developer-focused, open-source fraud detection products. The Senior Technical Product Manager will own the strategy and execution of the Bot Detection offering, including detection capabilities, automation and intent intelligence, customer-facing dashboard and API experiences, commercialization, and cross-functional delivery.

Responsibilities:

• 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)
• 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
• Drive strategy for our Automation Intelligence API, detecting automation without requiring a browser JS agent, including edge / pre-origin and direct HTTP contexts
• Align data contracts and platform requirements so bot/automation signals are consistent across JS-based and non-JS collection paths
• Working with other product managers, define and ship customer-facing product surfaces for Bot Detection:
• Dashboard experiences (overview, events, details, export/workflows)
• APIs and schema contracts (including compatibility where needed)
• Guidance for integrating Bot Detection into rule engines, fraud tooling, and customer decisioning pipelines
• Own plan gating / packaging assumptions for self-serve vs enterprise experiences
• Partner with Sales, CS, and Marketing to:
• Define value messaging and positioning
• Run beta/research preview motions and customer feedback loops
• Drive launch planning and enablement
• Work cross-functionally on pricing/packaging inputs and operational readiness (while partnering with the owning teams for billing implementation)
• Operate as the “single-threaded owner” across Product, Engineering, Data Science, Design, GTM, and Customer Success
• Run quarterly planning, define clear milestones, manage dependencies, and communicate tradeoffs

Requirements:

• 3+ years experience at, or deep familiarity with, bot management / fraud / abuse platforms (e.g., Cloudflare, Akamai, HUMAN, Arkose Labs, DataDome, PerimeterX, Sift, etc.)
• High ownership and strong cross-functional operating cadence (alignment, prioritization, and execution)
• Customer empathy and ability to translate customer problems into roadmap and requirements
• Strong data-informed decision making (defining success metrics, reading dashboards, and partnering with Product Analytics / DS)
• Comfort with measurement and iteration in detection systems: precision/recall tradeoffs, false positive/false negative analysis, coverage targets, and model/rule iteration loops
• Familiarity with privacy and regulatory constraints (e.g., GDPR/CCPA) as applied to detection signals; ability to drive privacy-preserving product requirements
• Deep understanding of web technologies and automation techniques (browser + API automation), and how detection systems behave under adversarial pressure
• Understanding of network and IP intelligence, including residential proxies and modern spoofing and tampering techniques
• Familiarity with detection signal pipelines (client-side instrumentation, server-side ingestion, feature engineering) and operating constraints (latency, scale, robustness)
• Experience with build vs buy evaluations in security/detection domains (and translating the outcome into an execution plan)
• Familiarity with privacy/security constraints and global compliance considerations (e.g., GDPR/CCPA) as applied to detection signals
• Experience building products that combine heuristic + ML approaches, and defining measurement for both