Staff Machine Learning Engineer(Platform - Identity)

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

Staff Machine Learning Engineer, Identity Verification

As a Staff Machine Learning Engineer on the Identity Verification team within the

What you’ll do:

• Own the full IDV ML stack, including document authenticity models, 1:1 and 1:Nface-match, liveness detection, presentation-attack detection, and deepfake/injection detection from feature pipeline through threshold tuning and production enforcement.
• 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.
• 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.
• 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.
• Lead and mentor senior and mid-level engineers in the pod while partnering with ML Platform and Risk ML teams to align cross-company ML system design.

Required Skills and Experience:

• 8+ years deploying production ML systems at scale, with proven technical leadership owning cross-team ML architecture from design through production.
• Domain experience in identity verification, biometrics, or account integrity with deep applied ML in at least two of: computer vision/biometrics, GNNs, sequence models, or NLP/LLMs.
• Expert-level Python with production experience in TensorFlow or PyTorch, including model training, evaluation, and serving infrastructure.
• Track record translating KYC/AML requirements and fraud trends into ML roadmaps and communicating trade-offs to Product, Compliance, Risk, and Security stakeholders.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Pay Transparency Notice:Base salary varies by location (see range below). Total compensation may also include equity and bonus eligibility, and benefits (medical, dental, vision, 401(k)).

Annual base salary range (excluding equity and bonus):$218,025—$256,500 USD