Staff ML Engineer, Identity Verification & Fraud Prevention

CryptoproNetwork; United States; Remote; USD 218,000 - 257,000; Full time

Job summary

CryptoproNetwork is hiring a Staff Machine Learning Engineer for Identity Verification. You will own the full ML stack for identity verification, including document authenticity, 1:1/1:N face matching, liveness, spoofing detection, and deepfake defenses.

You will build identity-graph systems with GNNs to detect synthetic-identity rings at onboarding. You will develop behavioral and device-intelligence models for real-time anomaly detection and device-based risk scoring, while benchmarking

Qualifications

• 8+ years deploying production ML systems at scale.
• Domain experience in identity verification, biometrics, or account integrity with deep ML in two areas (vision/biometrics, GNNs, sequences, or NLP/LLMs).
• Expert-level Python with production experience in TensorFlow or PyTorch.
• Translate fraud trends and compliance needs into ML roadmaps for cross-functional partners.
• Use generative AI with human oversight to improve workflows and outputs.

Responsibilities

• Own the full IDV ML stack from features to production enforcement.
• Build identity-graph systems using GNNs to detect synthetic identities.
• Develop behavioral and device-intelligence models for real-time risk scoring.
• Benchmark external models vs Coinbase-owned evaluation sets and route across providers/geographies.
• Lead and mentor engineers within the pod and partner with ML Platform and Risk ML teams.

Skills

8+ years production ML

Python (production)

TensorFlow/PyTorch

KYC/AML domain knowledge

communication with stakeholders

Tools

TensorFlow

PyTorch

Job description

CryptoproNetwork is hiring a Staff Machine Learning Engineer for Identity Verification. You will own the full ML stack for identity verification, including document authenticity, 1:1/1:N face matching, liveness, spoofing detection, and deepfake defenses.

You will build identity-graph systems with GNNs to detect synthetic-identity rings at onboarding. You will develop behavioral and device-intelligence models for real-time anomaly detection and device-based risk scoring, while benchmarking