Machine Learning Engineer, Coinbase

• 4+ years of professional experience in software engineering and/or AI/ML, with experience deploying AI/ML systems into production
• Passion & Values: A commitment to building an open financial system and a strong desire to protect users from fraud and scams. You embody our core cultural values: add positive energy, communicate clearly, be curious, and be a builder
• AI/ML Knowledge: Familiarity with applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection)
• Technical & Coding Skills: Proficient coding skills (e.g., Python) with experience in AI/ML frameworks (TensorFlow, PyTorch). Experience in building backend systems with a focus on data processing or analytics is a plus
• Team Collaboration: Ability to work collaboratively on technical initiatives and contribute to impactful AI/ML solutions
• Communication Skills: Strong communication skills, with the ability to convey technical concepts to both technical and non-technical audiences

Desirable

• We are looking for a Machine Learning Engineer to join our Risk AI/ML team. We are dedicated to building the sophisticated models that protect our customers and make Coinbase the most trusted platform in crypto. Your work will directly prevent fraud, account takeovers, and scams, while enabling future products like instant guest checkout and Coinbase Cards
• This is a hands-on technical role where you will leverage our cutting-edge, self-service ML platform to rapidly design, deploy, and iterate on the models that defend millions of users
• Focus on Modeling: Use our centralized, self-service ML platform to own the end-to-end development of ML models, from ideation to production
• Improve Core Model Performance: Join a high-priority "pod" to enhance our core models, including the Scam Models, Transfer/Transaction Risk Models, Withdrawal Limit Models, and Account Takeover models
• Rapidly Respond to Threats: Act on new threat data (identified by our Risk Operations partners) to build, train, and deploy permanent ML models that replace temporary rules—targeting a deploy-to-production timeline of under one week
• Build & Deploy Scalable Models: Develop production-grade AI/ML models and pipelines that enable reliable, real-time predictions, leveraging our platform's automated CI/CD pipelines and centralized feature store
• Apply Advanced ML: Apply modern methodologies (e.g., deep learning, NLP, Graph Neural Networks (GNNs), sequence modeling, and LLMs for NLP and conversational agents) to solve complex, crypto-native challenges—all while focusing 100% on modeling, not infrastructure plumbing
• Build Context-Aware Risk Systems: Go beyond a single score. You will help build the adaptive logic that decides which friction (a quiz, an LLM agent, a human review) to apply to which user (e.g., new user, high-value trader), balancing security with user experience
• Collaborate and Execute: Work closely with stakeholders from Risk Operations, Platform Engineering, and Product Management to close the feedback loop, turning new threats into automated defenses