Engineering Manager

Fingerprint builds the internet's most accurate device intelligence platform – used by thousands of developers and enterprises to detect fraud, stop account takeovers, and build trust into their products. Our engineering teams sit at the core of that mission: the signals we ship, the models we train, the APIs we maintain, and the data infrastructure we run all directly impact the accuracy and reliability that our customers depend on.

We are hiring Engineering Managers across four of our product and platform teams. Each role comes with a different technical domain and team context, but the leadership bar is the same: you own execution, you develop your people, and you stay technically grounded enough to earn the trust of senior engineers.

The Teams

Smart Signals — You'll lead the team responsible for Fingerprint's fraud and device intelligence signals: Bot Detection, Incognito Detection, suspect score, fraud score, and more. These signals are Fingerprint's primary product differentiator. Every signal you ship directly impacts ARR. As the threat landscape shifts toward agentic AI attacks and telemetry tampering, this team's work has never been more strategically critical.

Core APIs — You'll lead the team that builds and scales the API services underpinning Fingerprint's core platform — Identification, Smart Signals, Billing, and more. This is high-stakes infrastructure work: the APIs your team owns are the foundation everything else runs on. You'll work closely with senior engineers on architectural decisions and scaling challenges that directly affect product reliability for enterprise customers. (UPDATE: This role has been filled, but the other teams listed still need EM's)

Identification Accuracy — You'll lead a multidisciplinary team of ML Engineers, Data Scientists, Analysts, and Analytics Engineers responsible for the ML model powering Fingerprint's flagship Identification API. This team owns accuracy, reliability, and continuous improvement of that model — directly impacting the trust enterprise customers place in our platform. If you thrive at the intersection of people leadership and applied ML, this is the role.

Data Platform & ML Ops — You'll lead the team that owns Fingerprint's data foundation: the internal data warehouse powering business intelligence and product analytics, and the full ML Ops lifecycle that turns raw signals into production models. Your team's reliability and scalability directly enables the Identification and Smart Signals products. This role spans two technical disciplines — data infrastructure and ML systems — and requires credibility on both sides.

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 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 your team's stack to review designs, challenge architectural decisions, and surface risks before they become incidents. Earn IC trust through substance, not just title.

Coordinate across teams. Represent your team in technical discussions across Engineering. Manage cross-team dependencies proactively and communicate tradeoffs clearly to PMs and leadership.

Shape your team's strategy. Partner with Engineering Leadership and Product on roadmap prioritization. Translate technical constraints and opportunities into executable plans that move the business forward.

What We're Looking For

Required

• 2–5+ years of engineering management experience, with direct ownership of performance management, career development, and hiring — not just running standups
• Experience managing teams of 4–10 engineers in a fast-paced, iterative environment
• Technical depth in your team's domain: you can review designs, challenge architectural decisions, and earn credibility with senior engineers through substance
• A delivery track record — concrete examples of quarters where your team shipped reliably, and what you specifically did when they didn't
• Strong cross-functional communication: you translate technical constraints into language PMs, leadership, and other stakeholders can act on

Nice-to-Have

• Experience in a high-growth B2B SaaS or API-first company
• Familiarity with fraud detection, identity, device intelligence, or trust & safety domains
• ML-in-production exposure: you've worked alongside ML engineers or data scientists shipping models and understand the operationalization challenges at a high level
• Experience with data infrastructure, ML systems, or both — particularly relevant for Data Platform & ML Ops and Identification Accuracy
• Familiarity with latency-sensitive or real-time 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. We set standard ranges for all US roles based on function, level, and geographic location, benchmarked against similar stage growth companies. To comply with local legislation and provide greater transparency, we share salary ranges on all job postings. However, these ranges are specific to the hiring location and may differ within or outside the US.