Remote Engineering Manager, Real-Time Signals

Fingerprint empowers developers to stop online fraud at the source. Fingerprint is a globally dispersed, 100% remote company with a strong open-source focus.

What You'll Do

• 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 trade-offs — know when to ship and when to slow down.
• Run tight processes: planning, incident response, on-call rotations, post-mortems.
• 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 trade-offs 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.

Who You Are

Required

• 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.

Nice-to-Have

• 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.

Compensation Range

For US-based employees, the cash compensation range for this role is $159,000 – $215,000. This range is specific to the hiring location and may differ within or outside the US. Offers vary depending on relevant experience, education, certifications/licenses, skills, training, and market conditions.

Due to regulatory and security reasons, there is a small number of countries where we cannot have Fingerprint teammates based. In addition, because Fingerprint is an all-remote company and people can join our workforce from almost any country, we do not sponsor visas. Fingerprint teammates need to be authorized to work from their home location.

Fingerprint is dedicated to creating an inclusive work environment for everyone. We embrace and celebrate the unique experiences, perspectives and cultural backgrounds that each employee brings to our workplace. Fingerprint strives to foster an environment where our employees feel respected, valued and empowered, and our team members are at the forefront in helping us promote and sustain an inclusive workplace. We highly encourage people from underrepresented groups in tech to apply.