- Company Name
- Coinbase
- Job Title
- Machine Learning Engineer, Risk AI/ML
- Job Description
-
Job Title: Machine Learning Engineer, Risk AI/ML
Role Summary: Design, develop, and deploy end‑to‑end machine learning models that detect and mitigate fraud, account takeovers, and other security threats for a large fintech platform. Leverage a self‑service ML platform to move models from ideation to production quickly (≤ 1 week for new threat pipelines).
Expectations:
- 4+ years of professional experience in software engineering and/or AI/ML with a proven track record of deploying models into production.
- Strong coding proficiency in Python and experience with TensorFlow, PyTorch, or equivalent frameworks.
- Ability to work collaboratively with Risk Operations, Platform Engineering, and Product teams; communicate technical concepts to both technical and non‑technical stakeholders.
Key Responsibilities:
1. Own the full lifecycle of risk models – ideation, feature engineering, training, evaluation, deployment, and monitoring.
2. Enhance core risk models (Scam, Transaction, Withdrawal Limit, Account Takeover) and build new models in response to emerging threats.
3. Develop production‑grade ML pipelines that support real‑time scoring, using automated CI/CD and centralized feature stores.
4. Apply advanced ML techniques (deep learning, NLP, GNNs, sequence models, LLMs) to complex crypto‑native challenges.
5. Create context‑aware risk logic that selects appropriate friction (quiz, LLM agent, manual review) based on user profile.
6. Collaborate with stakeholders to close feedback loops, translating operational insights into automated defenses.
Required Skills:
- Python programming, TensorFlow/PyTorch.
- Experience building scalable ML pipelines and deploying models (e.g., Edge, RayServe, Airflow, Spark, Kafka).
- Familiarity with feature stores (Tecton or similar), model serving, and MLOps best practices (monitoring, retraining).
- Strong analytical mindset, problem‑solving, and ability to work in a fast‑paced environment.
- Excellent written and verbal communication.
Required Education & Certifications:
- Bachelor’s degree in Computer Science, Data Science, AI/ML, or related field.
- Preference for experience with Graph Neural Networks, LSTM/sequence models, or LLM fine‑tuning and reinforcement learning.
- Knowledge of data analysis/visualization tools is a plus.