Staff Machine Learning Engineer(Platform - Identity)

About the Role

The Identity Verification team is part of the Platform group, which is responsible for developing and maintaining the core infrastructure of Coinbase. As a Staff Machine Learning Engineer, you will lead the technical strategy for IDV ML, working closely with cross-functional teams to align on ML system design. You will also have the opportunity to mentor senior and mid-level engineers, helping to develop their skills and expertise.

What You Will 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.
• Collaborate with the Product, Compliance, Risk, and Security teams to communicate trade-offs and ensure ML systems meet business and regulatory requirements.
• Stay up-to-date with the latest advancements in ML and identity verification, applying this knowledge to continuously improve Coinbase's IDV systems.
• Develop and maintain technical documentation of ML systems, including architecture, data pipelines, and model performance metrics.
• Participate in code reviews and contribute to the improvement of the overall code quality and engineering practices.

What We Are Looking For

• 8+ years of experience 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.
• Strong understanding of ML principles, including model interpretability, explainability, and fairness.
• Experience with cloud-based infrastructure and containerization (e.g., Docker, Kubernetes).
• Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams.
• Track record of translating KYC/AML requirements and fraud trends into ML roadmaps and communicating trade-offs to stakeholders.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Nice to Have

• Experience with identity verification platforms and technologies (e.g., Onfido, Jumio).
• Knowledge of cybersecurity principles and practices, particularly in the context of ML systems.
• Familiarity with agile development methodologies and version control systems (e.g., Git).
• Experience with data visualization tools (e.g., Tableau, Power BI) and data science platforms (e.g., Jupyter Notebooks).

Benefits and Perks

• Competitive salary and equity package.
• Comprehensive health, dental, and vision insurance.
• 401(k) matching program.
• Flexible PTO policy and paid holidays.
• Remote work stipend and equipment budget.
• Opportunities for professional development and growth.
• Access to cutting-edge technologies and tools.
• Collaborative and dynamic work environment.

• Tip: Highlight your experience with ML frameworks like TensorFlow or PyTorch, and be prepared to discuss your approach to model training and evaluation.
• Develop a strong understanding of identity verification principles and practices, including biometrics and computer vision.
• Showcase your ability to communicate complex technical concepts to non-technical stakeholders, including product managers and compliance teams.
• Be prepared to discuss your experience with cloud-based infrastructure and containerization, and how you have used these technologies to deploy ML systems at scale.
• Consider creating a portfolio of your work, including examples of ML models you have developed and deployed, to demonstrate your skills to potential employers.
• When negotiating salary, be sure to research the market rate for your role and experience level, and be prepared to discuss your expectations and requirements.