Engineering Manager, Smart Signals

Fingerprint empowers developers to stop online fraud at the source. They are seeking a Smart Signals Engineering Manager to lead the Smart Signals team, focusing on technical delivery, team leadership, and execution of fraud detection capabilities.

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 — especially engineers approaching Staff
• Drive execution. Own sprint planning and roadmap delivery. Manage scope, urgency, and quality tradeoffs — 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

Requirements:

• Engineering management experience: 4+ years managing a team of 8–10 engineers. You have owned performance management, career conversations, and hiring — not just run standups
• Technical depth in backend systems: You have built or operated production backend services at scale. You 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. You can diagnose why a team slips and what you specifically did about it
• Cross-functional collaboration: Direct experience working with PMs and research or data partners to translate requirements into executable engineering plans. You manage dependencies proactively
• Fraud detection or security signals familiarity: Some exposure to fraud, risk, or device intelligence systems — enough to understand what false positives and false negatives mean for product quality. Deep expertise is not required; curiosity about the space and the ability to ramp quickly is what matters
• ML-in-production exposure: You've worked alongside ML engineers or data scientists shipping models to production and understand the operationalization challenges at a high level — model drift, serving infrastructure, rollback — even if you weren't building it yourself
• 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 p95/p99 latency is a product quality metric