Smart Signals Engineering Manager
Fingerprint’s Smart Signals team is the front line of our fraud and device intelligence capabilities. We develop signals such as Bot Detection, Browser Tampering Detection, Incognito Detection, suspect scores, fraud scores, and more, that enable developers to build secure, fraud‑resistant applications. As the fraud landscape shifts toward agentic AI attacks and telemetry tampering, this work has become strategically critical. Earn the opportunity to directly influence product differentiation and ARR through every signal you ship.
What You’ll Do
- Lead the team. Own 1:1s, performance calibrations, career development, hiring, and set the bar for code quality, on‑call discipline, and delivery.
- Drive execution. Own sprint planning and roadmap delivery; manage scope, urgency, and quality trade‑offs; run tight processes for planning, incident response, on‑call rotations, and post‑mortems.
- Stay technically grounded. Maintain sufficient depth in the signal stack to review designs, challenge architecture decisions, and surface risks before incidents occur; 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 trade‑offs clearly to PMs and leadership.
- Shape signal strategy. Partner with Engineering Leadership and Product on roadmap prioritization; translate research findings into executable engineering plans; contribute to detection strategy as the threat landscape evolves.
Who You Are
Required
- Engineering management experience: 4+ years managing a team of 8–10 engineers, owning performance management, career conversations, and hiring—not just running stand‑ups.
- Technical depth in backend systems: Built or operated production back‑end services at scale; can review code, challenge architecture decisions, and earn credibility with senior engineers through substance.
- Delivery track record: Concrete examples—with outcomes and numbers—of quarters where your team shipped reliably; able to diagnose why a team slips and take corrective action.
- Cross‑functional collaboration: Direct experience working with PMs and research or data partners to translate requirements into executable engineering plans; proactively manage dependencies.
Nice‑to‑Have
- Fraud detection or security signals familiarity: Some exposure to fraud, risk, or device intelligence systems—enough to understand false positives and false negatives—where curiosity and rapid ramp‑up are valued.
- ML‑in‑production exposure: Worked alongside ML engineers or data scientists shipping models to production and understands operationalization challenges such as model drift, serving infrastructure, and rollback.
- High‑growth B2B SaaS or API‑first company experience.
- Real‑time or latency‑sensitive systems: Experience managing engineers working on signal pipelines, edge computation, or systems where 95th/99th percentile latency is a product quality metric.
Compensation Range
For U.S.‑based employees, the cash compensation range for this role is $159,000 – $215,000. This range is based on function, level, and geographic location. Pay packages may vary based on experience, skills, certifications, and market conditions.
