Staff Machine Learning Engineer (Platform - Identity)

About the role

This role focuses on leading the Identity Verification (IDV) Machine Learning team at Coinbase. The engineer will be responsible for the full lifecycle of ML systems that verify user identities and detect fraudulent activities. This includes developing models for document authenticity, facial recognition, liveness detection, and identifying sophisticated attacks like deepfakes and synthetic identities. The work directly contributes to the security and integrity of millions of user accounts.

A key part of this position involves building identity-graph systems using Graph Neural Networks (GNNs) to identify coordinated fraud rings by clustering accounts based on biometric, device, and document signals. Additionally, the role requires developing real-time behavioral and device intelligence models to detect anomalies, classify human vs. bot interactions, and score risk based on device fingerprints. These systems are crucial for preventing fraud at the onboarding stage and during ongoing account activity.

Beyond direct model development, the Staff ML Engineer will drive the vendor ML strategy by evaluating external models against internal benchmarks and designing dynamic routing logic across different providers and geographies. This involves creating an in-house evaluation layer to catch regressions before they impact users. The role also includes mentoring other engineers and collaborating with ML Platform and Risk ML teams to ensure alignment in system design across the company.

The annual base salary for this position ranges from $218,025 to $256,500 USD, with total compensation potentially including equity and bonus eligibility.

Skills that matter here

• Python: Expert-level proficiency is required for developing and deploying ML systems, including model training, evaluation, and serving infrastructure.
• TensorFlow: Production experience with this framework is necessary for building and managing machine learning models.
• PyTorch: Production experience with this framework is necessary for building and managing machine learning models.
• ML systems: The role requires extensive experience in deploying and leading the architecture of production machine learning systems at scale.
• computer vision: Applied deep ML experience in this domain is needed for tasks such as document authenticity, face-matching, and liveness detection.
• biometrics: Domain expertise and deep applied ML experience in biometrics are crucial for developing identity verification and fraud detection models.

Who this role suits

• A candidate with 8+ years of experience deploying production ML systems, demonstrating technical leadership in cross-team ML architecture.
• Someone with domain expertise in identity verification, biometrics, or account integrity.
• An individual who can translate compliance requirements and fraud trends into ML roadmaps and communicate technical trade-offs to various stakeholders.
• A person who can responsibly utilize generative AI while maintaining human oversight to deliver business-ready outputs and drive measurable improvements.

From the employer

• 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.

• 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.

• 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.
• Coinbase is proud to be an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, sex, gender expression or identity, sexual orientation or any other basis protected by applicable law.