Staff ML Engineer, Identity Verification — Remote-First

Coinbase; Lincoln (NE); On-site; USD 218,000 - 257,000; Full time

Job summary

Coinbase is seeking a Staff Machine Learning Engineer for the Identity Verification team within the Platform group. You’ll own ML systems that verify identity, documents, and capture sessions, critical to signups, recoveries, and high-risk actions across the platform.

You’ll lead end-to-end ML strategy from architecture to production enforcement, protecting the integrity of millions of accounts while collaborating with ML Platform and Risk teams and mentoring engineers in the pod.

Qualifications

• 8+ years deploying production ML systems at scale with cross-team leadership.
• Domain experience in identity verification, biometrics, or account integrity.
• Expert-level Python with production TensorFlow or PyTorch.
• Experience translating KYC/AML requirements into ML roadmaps.
• Ability to communicate trade-offs to multiple stakeholders and lead projects.

Responsibilities

• Own the full IDV ML stack from feature pipeline through production enforcement.
• Build identity-graph systems to detect synthetic-identity rings.
• Develop behavioral and device-intelligence models for real-time risk scoring.
• Benchmark external models and design routing across providers/geographies.
• Lead and mentor engineers while collaborating with ML Platform and Risk teams.

Skills

Production ML systems

Identity verification / biometrics

Python production experience

KYC/AML domain knowledge

Generative AI governance

Tools

TensorFlow

PyTorch

GNNs

Job description

Coinbase is seeking a Staff Machine Learning Engineer for the Identity Verification team within the Platform group. You’ll own ML systems that verify identity, documents, and capture sessions, critical to signups, recoveries, and high-risk actions across the platform.

You’ll lead end-to-end ML strategy from architecture to production enforcement, protecting the integrity of millions of accounts while collaborating with ML Platform and Risk teams and mentoring engineers in the pod.