Founding Device Fingerprinting Engineer

About the Role

Fingerprinting is becoming a first-class pillar of Prelude's fraud detection engine — and this is the founding role that will define it. You will own the entire fingerprinting surface: architecture, research, production systems, and eventually the team we build around you. You'll report directly to leadership and shape the roadmap from scratch.

Fraud is an adversarial game. Attackers use emulators, residential proxies, VPNs, device spoofing, and anti-detect tooling to flood our customers' authentication pipelines with fake traffic. Your job is to make that invisible — or at least expensive enough not to be worth it.

You'll build the systems that collect, process, and exploit device and network signals at scale: from raw hardware attributes and TLS fingerprints to IP risk scoring and carrier intelligence. You'll combine classical feature engineering with ML to produce high-fidelity risk signals that feed our real-time scoring engine — processing millions of authentication attempts per day in milliseconds.

This role sits at the intersection of security research, systems engineering, and applied machine learning. It's for someone who thinks like an attacker, builds like an engineer, and reasons like a data scientist.

What You'll Be Doing:

• Owning the fingerprinting roadmap end to end — from signal collection in our mobile SDKs (iOS, Android) and server-side APIs, to the feature engineering pipelines that turn raw attributes into fraud signals
• Building and hardening device fingerprinting systems — persistent device identity across app reinstalls, rooted/emulated device detection, hardware attestation, and spoofing resistance for both web and mobile contexts
• Developing network intelligence signals — IP reputation scoring, proxy/VPN/datacenter detection, TLS/JA4 fingerprinting, carrier and MCCMNC enrichment, and residential vs. commercial traffic classification
• Applying ML to fingerprinting problems — anomaly detection on device attribute distributions, clustering to identify fraud rings sharing infrastructure, supervised classification of suspicious signal patterns, and adversarial robustness against evolving evasion techniques
• Integrating with our real-time scoring engine — producing low-latency features that enrich our per-authentication risk model under strict latency constraints
• Researching attacker techniques — reverse-engineering anti-detect tooling, automation frameworks, and bypass techniques to stay ahead of the adversarial curve
• Laying the groundwork for a team — defining engineering standards, research practices, and data pipelines that will scale as we hire around you.

What We're Looking For:

• 5+ years of experience in a relevant field — fraud detection, bot mitigation, mobile security, or detection engineering; you've shipped systems in production, not just research prototypes
• Deep knowledge of device fingerprinting — mobile hardware signals, OS-level attestation (Play Integrity, DeviceCheck, SafetyNet), browser fingerprinting APIs, and spoofing/emulation detection
• Hands-on experience with network intelligence — TLS fingerprinting (JA3/JA4), IP enrichment pipelines, proxy/VPN classification, AS number analysis, and carrier-level signals
• Applied ML experience in a security context — feature engineering, anomaly detection, clustering, and classification with end-to-end ownership from training through production deployment
• An adversarial mindset — you know the attacker landscape (emulators, spoofing techniques, anti-detect browsers, residential proxy networks) and design systems with bypass attempts already in mind
• A preference for simple, robust designs over clever, fragile ones — with strong intuition for what will hold up at scale under active adversarial pressure
• Fluent English, given our international team and customer base

Nice to have:

• Familiarity with phone/SIM authentication context, GSMA intelligence, or telco APIs
• Experience with JA4 or other network fingerprinting techniques
• Proficiency in Go, Rust, or Python for backend/data work
• Experience with stream processing systems (SQS, Kafka) or OLAP stacks (ClickHouse, Redshift)
• iOS or Android SDK internals knowledge
• Published research, open-source contributions, or technical writing in the fraud/bot detection space.

What Can We Offer You?

• Competitive compensation package with BSPCEs
• Flexible remote policy
• 100% travel to the office subsidized
• Health insurance via Alan
• Urban Sports Club gym membership
• We will provide you with the gear you need for your role (a laptop and a phone, for on-call rotations)
• Swile meal vouchers
• Free snacks and drinks in the office
• An annual offsite in a great location (last one was at La Pradet!)
• The opportunity to build something from 0-1, and make an impact every day