Staff Machine Learning Engineer (Platform - Identity)

About the role

In this role, you will be responsible for the entire identity verification machine learning stack, which includes various models for document authenticity, face matching, and anomaly detection. You will work on building identity-graph systems to combat fraud and improve account integrity.

You will collaborate with other teams, mentor engineers, and drive the ML strategy for vendor models. Success in this position means effectively translating complex requirements into actionable ML roadmaps and ensuring high-quality outputs.

The base salary for this role ranges from $218,025 to $256,500, depending on location.

Skills that matter here

• Python: This role requires expert-level Python skills for developing and deploying ML models.
• TensorFlow: Experience with TensorFlow is essential for model training and serving infrastructure.
• PyTorch: Knowledge of PyTorch is necessary for building and evaluating machine learning systems.
• ML systems: You will own the full machine learning stack, ensuring robust deployment and performance.
• computer vision: The role involves applying computer vision techniques for identity verification.
• biometrics: Experience in biometrics is crucial for building models that enhance account security.

Who this role suits

• A strong technical leader with over 8 years of experience in deploying production ML systems.
• Someone who is comfortable working in a fast-paced and demanding environment.
• An individual with a collaborative mindset who enjoys mentoring others and sharing knowledge.
• A candidate who has a solid understanding of identity verification and fraud prevention in the ML context.

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.