Staff Machine Learning Engineer(Platform - Identity)

Amit érdemes tudni

• Remote · Korlátozott
• A távmunka az Egyesült Államokra korlátozódik.
• Szint: Lead
• Fizetés: nem közölt

Helyszín és jogosultság

A távmunka az Egyesült Államokra korlátozódik.

Remote - USA

Megbízhatóság: 92%. A végső döntés a munkáltatóé – mindig olvasd el az eredeti hirdetést.

Pozíció

<div class="content-intro"><p>Ready to do the most impactful work of your career? At&nbsp;<a href="https://www.coinbase.com/?utm_campaign=mt_o_m_w_m_m__coi_0_jd-onchain&amp;utm_source=coinbase">Coinbase</a>, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.”&nbsp;<a href="https://www.coinbase.com/careers">learn more about working at Coinbase</a>.</p></div><h1>Staff Machine Learning Engineer, Identity Verification</h1> <p class="mb-2 whitespace-pre-wrap">As a Staff Machine Learning Engineer on the Identity Verification team within the&nbsp;<a href="https://www.coinbase.com/careers/product-groups">Platform</a>&nbsp;group, you'll own the ML systems that determine whether a person, document, and capture session are legitimate. Every signup, account recovery, and high-risk action at Coinbase depends on these models. You'll lead the technical strategy for IDV ML end-to-end, from architecture through production enforcement, protecting the integrity of millions of accounts.</p> <p class="mb-2 whitespace-pre-wrap"><strong>What you'll do:</strong></p> <ul> <li>Own the full IDV ML stack, including document authenticity models, 1:1 and 1:N&nbsp;face-match, liveness detection, presentation-attack detection, and deepfake/injection detection from feature pipeline through threshold tuning and production enforcement.</li> <li>Build identity-graph systems using GNNs that cluster accounts sharing biometric, device, and document signals to detect synthetic-identity rings and coordinated fraud at onboarding.</li> <li>Develop behavioral and device-intelligence models for capture-session anomaly detection, bot-vs-human classification, and device-fingerprint-based risk scoring at real-time latency.</li> <li>Drive vendor ML strategy by benchmarking external models against a Coinbase-owned evaluation set, designing dynamic routing logic across providers and geographies, and building the in-house evaluation layer that catches regressions before they reach users.</li> <li>Lead and mentor senior and mid-level engineers in the pod while partnering wit

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