Engineering Manager, Fingerprint

• Engineering management experience: 3+ years managing software engineers, ideally including senior and staff-level ICs, with a track record of shipping products — not just features — through launch
• Strong technical foundation: 8+ years in software engineering before or alongside management. You can read a design doc and a PR critically, engage senior engineers on architecture, and make technical calls when the team is split
• Relevant domain depth in at least one of: bot detection or bot management, fraud or abuse prevention, web security, browser or client-side engineering, or high-volume API services. You understand that this is an adversarial problem where the other side adapts
• Quality-driven mindset: Experience building or running measurement-driven engineering — test corpora, precision/recall or FP/FN tracking, regression suites, SLOs — and using that data to prioritize
• Hands-on with AI coding tools: You use AI coding agents in your own work, have opinions about where they help and where they don't, and are excited to lead a team where they are the default
• Cross-functional operator: You've worked closely with product managers, researchers, and customer-facing teams, and you're comfortable making dependencies and trade-offs explicit rather than absorbing them
• Clear communicator: You write well, make decisions visible with your reasoning, and can explain detection quality and launch readiness to executives and customers alike

Desirable

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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 detection service
• This is a hands-on-adjacent engineering manager role for a small, senior, high-leverage team. You are not expected to write the production code — your engineers and their agents do that — but you are expected to be close enough to it to set direction, evaluate the work, and hold the quality bar
• The team has unusual autonomy and unusual visibility. It owns a product that leadership considers strategically critical, it is the first team operating Fingerprint's AI-native engineering model, and its results — good and bad — will be used as evidence for how the rest of engineering works. That means two things: you'll have real room to decide how the team operates, and you'll need to be honest about what the data shows
• In the first 90 days you will likely: finalize the roster and ownership lines with the sibling team, get the quality loop and engineering baselines running, deliver the engineering evidence for the Beta gate, and run the first review of the AI-native operating model
• A problem that is getting harder and more important every quarter: The internet is filling with agents. Customers need to tell useful automation from abuse, and the answer isn't "block everything." The team that solves this well defines a category
• A product with a clear path to market: Beta to GA to standalone launch, with product, go-to-market, and executive support already lined up
• A senior team and a dedicated PM from day one: You're not starting from zero. The engineers already know the problem; your job is to focus them and remove what's in their way
• Lead how engineering will work next: AI-native development, contract-first APIs, and a self-directed team model — piloted here first, with your findings shaping the whole organization
• VP direct line: You report to the VP of Engineering, set your own goals against a clear charter, and bring recommendations rather than requests for direction