Staff Machine Learning Engineer(Platform - Identity)

About the role

In this role, you will manage the entire ML stack for identity verification, focusing on models for document authenticity, face matching, and anomaly detection. You will also develop systems to identify synthetic identities and enhance fraud detection.

You will collaborate with various teams, mentoring engineers while driving the ML strategy for vendor models and ensuring the integrity of Coinbase's identity verification processes. Success in this position involves delivering robust ML solutions that enhance account security.

The salary range for this role is between $150,000 and $250,000, depending on experience and location.

Skills that matter here

• Machine Learning: You will own the full IDV ML stack, ensuring its effectiveness in maintaining account integrity.
• Python: Expert-level Python is required for developing and deploying ML models.
• TensorFlow: Experience with TensorFlow is essential for model training and serving.
• PyTorch: You will utilize PyTorch for building and evaluating ML infrastructure.
• Computer Vision: Knowledge in computer vision is critical for developing face-matching and liveness detection models.
• Biometrics: You will apply biometrics expertise to enhance identity verification processes.

Who this role suits

• A seasoned professional with over 8 years of experience in deploying ML systems at scale.
• Someone with a strong background in identity verification and account integrity.
• A leader who can effectively mentor others and communicate complex ML concepts to various stakeholders.
• An individual who approaches generative AI with responsibility and emphasizes human oversight.

From the employer

What you'll do:

• Own the full IDV ML stack, including document authenticity models, 1:1 and 1:N face-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.