Engineering Manager, Automation Intelligence

About this role

Fingerprint empowers enterprises to detect and stop online fraud with the world’s most accurate device intelligence. We lead our industry with bleeding-edge identification capabilities and work on turning new ideas and discoveries in the fraud detection space into reality. Our customers range from innovative startups to leading enterprise companies, including Plaid, Dropbox, and Booking.com.

Fingerprint is a globally dispersed, 100% remote company. We were named on on the 2026 Forbes Best Startup Employers list and ranked #803 on the 2026 Inc. 5000 list of America’s fastest-growing private companies.

We have raised $77M and are backed by Craft Ventures (Tesla, Facebook, Airbnb ), Nexus Venture Partners ( Postman, Apollo.io,MinIO, Druva) and Uncorrelated Ventures ( Redis, Rollbar, Gradle).

The Role in Context

Fingerprint's device intelligence platform processes billions of identification events for companies that need to know who — or what — is on the other end of a request. Increasingly, the answer is not a person. AI agents, AI assistants, crawlers, headless browsers, and malicious automation now make up a large and fast-growing share of internet traffic, and telling them apart from humans — and from each other — is becoming one of the most important problems our customers ask us to solve.

We are standing up a dedicated Automation Intelligence engineering team to own that problem end to end: detection in the browser, detection at the edge, the classification taxonomy that tells customers what kind of automation they're seeing and how confident we are, and the API that customers and their agents call.

We're looking for an Engineering Manager to lead this team. You'll inherit a small, senior group (three engineers to start, growing to four or five) and a clear mandate. You report directly to the VP of Engineering.

Your Mission

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Ship the product — Take the automation intelligence product through Beta to GA, and then to a standalone commercial launch. You own engineering readiness: scope, estimates, service reliability, operating limits, API and MCP coverage, and the evidence that it's ready

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Make detection accurate and keep it that way — Build the team's quality loop: a test corpus of known bots, agents, and automation frameworks; continuous false-positive and false-negative testing; a regression test for every confirmed gap; and coverage benchmarked against the best in the industry. Detection quality is the product

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Build a team that runs itself — Set the team up so adding a detection, shipping a change, or running a staged rollout doesn't depend on another team's calendar. Grow the engineers, hire the next seats, and create an environment where senior people do their best work

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Lead the AI-native engineering model — AI agents write the code by default and engineers decide what gets built and whether it's right. You'll operate that model, measure it honestly, and produce the playbook the rest of engineering adopts

What You'll Do

Delivery & Product Readiness

• Own the engineering plan for Beta → GA → standalone launch: PRD partnership with the product manager, estimates, sequencing, and the readiness evidence that gates each stage

• Define and prove the service's operating envelope — throughput, latency tails, rate limiting, availability SLOs — before customers depend on it

• Run staged rollouts for detection changes that affect customer bot rates, with feature flags, rollback, and clear go/no-go evidence for customer-facing teams

• Turn design-partner feedback and customer-reported false positives into prioritized, owned engineering work

Detection Quality & Coverage

• Stand up and run a weekly detection quality loop: prioritized test corpus, automated FP/FN testing across browser and edge detection, regression coverage, and drift monitoring

• Drive coverage of the automation that matters most to customers — AI agents and assistants first — benchmarked against industry leaders, and prioritized by customer exposure rather than catalogue size

• Guide the hard problems: self-hosted agents, stealth and anti-detect browser automation, and fingerprinting agent infrastructure across browser, network, and runtime evidence

• Evolve the classification contract from "automation detected" to rich agent context — automation type, verified vs. inferred identity, confidence, and reason codes — and keep detection and attribution measured separately

Team Leadership

• Manage, coach, and grow a team of senior engineers; run hiring for the next seats, starting with a detection research and quality engineer

• Set the team's goals against its charter and bring them to leadership for alignment, not approval; communicate decisions as intent with reasoning before acting

• Establish healthy engineering practice from day one: written ownership lines, review tiers per repository, specs before epics, and baselines for cycle time, throughput, predictability, change failure rate, and recovery time

• Negotiate clean working agreements with the sibling team the codebase is shared with today, and drive gradual decoupling prioritized by what blocks independent deploys

AI-Native Engineering

• Operate the agent-first development model: spec → coding agent → review agent → security review → human merge, with low-risk changes merging on agent approval and mandatory human review on critical paths

• Treat the agent harness — repository instructions, skills, review personas — as infrastructure maintained like code, and publish it so other teams start ahead

• Measure the model against the team's own baseline, run 30- and 60-day reviews, and write up what worked and what didn't for the rest of engineering

Contract-First APIs

• Ensure every surface the team ships is contract-first: OpenAPI in the repo, breaking-change detection in CI, generated docs and SDKs, and the API kept readable by both developers and AI agents

• Own the team's observability: logs, metrics, traces, and SLOs for the…