C
Coinbase
Staff Machine Learning Engineer (Platform - Identity)
Skills
PythonPyTorchTensorFlowMachine LearningAINLP / Natural Language ProcessingCompliance
23 days ago
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Important information
Contract type:
Permanent
Salary:
$218,025 – $256,500 / year
Location:
United States
Work mode:
Onsite
Published on:
27 September 2026
What they need
- As a Staff Machine Learning Engineer on the Identity Verification team within the Platform group, you’ll own the ML systems that determine whether a person, document, and capture session are legitimate. Every signup, account recovery, and high-risk action at Coinbase depends on these models. You’ll lead the technical strategy for IDV ML end-to-end, from architecture through production enforcement, protecting the integrity of millions of accounts
- 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- Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality
- Expert-level Python with production experience in TensorFlow or PyTorch, including model training, evaluation, and serving infrastructure
- 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
- Track record translating KYC/AML requirements and fraud trends into ML roadmaps and communicating trade-offs to Product, Compliance, Risk, and Security stakeholders
- 8+ years deploying production ML systems at scale, with proven technical leadership owning cross-team ML architecture from design through production
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