Fingerprint
Smart Signals Engineering Manager
Fingerprint
$159k - $215k
Worldwide (Remote)

Smart Signals Engineering Manager

Overview

Fingerprint empowers developers to stop online fraud at the source. We work on turning radical new ideas in the fraud detection space into reality.

Job Description

Fingerprint's Smart Signals team is the front line of our fraud and device intelligence capabilities. We produce the signals β€” Bot Detection, Browser tampering detection, Incognito Detection, suspect score, fraud score and much more β€” that developers use to build secure, fraud-resistant applications.

Responsibilities

  • - Lead the team. Own 1:1s, performance calibrations, career development, and hiring.
  • - Set the bar for code quality, on-call discipline, and delivery.
  • - Coach intentionally β€” especially engineers approaching Staff.
  • - Drive execution. Own sprint planning and roadmap delivery.
  • - Manage scope, urgency, and quality tradeoffs β€” know when to ship and when to slow down.
  • - Run tight processes: planning, incident response, on-call rotations, postmortems.
  • - Stay technically grounded. Maintain enough depth in the signals stack to review designs, challenge architectural decisions, and surface risks before they become incidents.
  • - Partner with ML Engineers on signal pipelines and model deployment.
  • - Coordinate across teams. Represent Smart Signals in technical discussions across Engineering.
  • - Manage cross-team dependencies proactively and communicate tradeoffs clearly to PMs and leadership.
  • - Shape signal strategy. Partner with Eng Leadership and Product on roadmap prioritization.
  • - Translate research findings into executable engineering plans.
  • - Contribute to detection strategy as the threat landscape evolves.

Required Skills

  • - Required Engineering management experience: 4+ years managing a team of 8–10 engineers.
  • - Technical depth in backend systems: You have built or operated production backend services at scale.
  • - Delivery track record: Concrete examples β€” with outcomes and numbers β€” of quarters where your team shipped reliably.
  • - Cross-functional collaboration: Direct experience working with PMs and research or data partners to translate requirements into executable engineering plans.
  • - Nice-to-Have Fraud detection or security signals familiarity: Some exposure to fraud, risk, or device intelligence systems.
  • - ML-in-production exposure: You've worked alongside ML engineers or data scientists shipping models to production.
  • - High-growth B2B SaaS or API-first company experience.
  • - Real-time or latency-sensitive systems: Experience managing engineers working on signal pipelines.

About the company

Identify every visitor. Stop fraud, detect bots, or delight customers. Identify good and bad visitors with industry-leading accuracy - even if they're anonymous.


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