Machine Learning Engineer - AI​/ML Risk Platform

Overview

Ready to be pushed beyond what you think you’re capable of? At Coinbase, our mission is to increase economic freedom in the world. It’s a massive, ambitious opportunity that demands the best of us, every day, as we build the emerging onchain platform — and with it, the future global financial system.

We’re seeking a candidate who is passionate about our mission and believes in the power of crypto and blockchain technology to update the financial system. We want someone who is eager to leave their mark, thrives under pressure, and actively seeks feedback to keep leveling up. Our work culture is intense and isn’t for everyone. If you want to build the future alongside others who excel in their disciplines, this is a place to be.

While many roles at Coinbase are remote-first, in-person participation is required throughout the year. Team and company-wide offsites are held multiple times annually to foster collaboration, connection, and alignment. Attendance is expected and fully supported.

The mission of the Platform Product Group engineers is to build a trusted, scalable and compliant platform to operate with speed, efficiency and quality. Our teams include Product Foundations (Identity, Payment, Risk, Proofing & Regulatory, Finhub), AI Platform, Core Automation, and Infrastructure. The AI Platform Team focuses on building centralized AI infrastructure and acceleration solutions that drive productivity, innovation, and customer impact across Coinbase.

As a Machine Learning Engineer on the AI Platform Team, you will play a pivotal role in transforming our capabilities by leveraging cutting-edge AI models, frameworks, and tools, as well as diverse data sources including blockchain data. Your work will focus on lowering barriers to cryptocurrency adoption while shaping the future of AI-powered interactions, including improving recommendations, chatbots, and other intelligent features to enhance product usability.

• Explore and apply advanced machine learning techniques, including large language models (LLMs), deep learning, and graph neural networks to solve complex challenges across the organization.
• Collaborate with management and stakeholders to define strategic roadmaps and translate them into actionable quarterly plans.
• Drive project execution by managing priorities, deadlines, and deliverables, leveraging technical expertise.
• Develop high-quality, well-tested Python code to address critical product and business needs.
• Design scalable, secure systems using state-of-the-art AI/ML technologies and industry best practices.
• Mentor team members on design principles, coding standards, and the adoption of AI productivity tools.
• Troubleshoot and resolve complex technical issues to improve system reliability, scalability, and operational efficiency.
• Ensure the security, scalability, and architectural integrity of feature designs through thorough reviews across teams.

• 10+ years of experience in machine learning and software engineering.
• Proven expertise in designing, building, scaling, and maintaining production services with a focus on data processing, machine learning, and analytics.
• Hands-on experience with at least one ML model type, such as LLMs, GNNs, deep learning, logistic regression, or gradient boosting trees.
• Proficiency in data mining, information retrieval, advanced statistics, natural language processing, or computer vision.
• Ability to write high-quality, well-tested code that effectively addresses customer needs.
• Experience mentoring and developing junior engineers by implementing processes and setting a high standard for excellence.
• Passion for building an open financial system that connects the world.
• Exceptional written and verbal communication skills with a commitment to open and transparent cultural practices.

• Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Data Mining, Statistics, or a related technical field.
• Proficiency with GenAI frameworks/tools and technologies such as Apache Airflow, Spark, Flink, Kafka/Kinesis, Snowflake, and Databricks.
• Familiarity with model interpretability and responsible AI practices.
• Expertise in data…