Staff Machine Learning Engineer - Risk AI/ML

Coinbase Careers Page

About The Position

The Risk AI/ML team has a critical mission: to foster business growth by effectively managing the financial threats impacting the business and our users. We are on the front lines, building the flexible, scalable, and intelligent systems required to detect and prevent fraud, scams, and account takeovers at scale. As a member of this team, you will build and deploy the sophisticated AI/ML models that power our real-time, near-real-time, and scheduled risk-detection workflows. You will collaborate closely with Platform, Product, Analyst, and Ops teams to uncover new fraud patterns, fight baddies, and continuously improve our defensive capabilities, protecting our good users and the open financial system. About the Role 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.

Requirements

8-10+ years of professional experience in software engineering and/or AI/ML, with experience deploying AI/ML systems into production.
Proven track record of technical leadership, including leading small teams or pods and designing and deploying large-scale, cross-team ML systems from scratch.
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: Deep expertise in applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection); with required prior domain experience in Payment Risk, Credit Risk, or Identity Risk / Account Takeover / Scams.
Technical & Coding Skills: Expert-level coding skills (e.g., Python) and deep experience with 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.

Nice To Haves

Master’s / Ph.D in Computer Science, AI/ML, Data Science, or a related field.
Familiarity with modern data and AI/ML infrastructure (e.g., Feature Stores like Tecton, Model Serving like RayServe, Apache Airflow, Spark, Kafka).
Experience with Graph Neural Networks (GNNs) or Sequential Models (like LSTMs).
Experience with LLMs (NLP, fine-tuning, agentic systems) or Reinforcement Learning.
Understanding of MLOps best practices, including monitoring and improving production models.
Experience with data analysis and visualization tools.

Responsibilities

Own a Critical Risk Domain: Take full technical ownership of a core problem space, such as Scams or Account Takeover. You will design, build, and lead the strategy for all models in this domain.
Architect and Design Systems: Lead the system design and architecture for new, complex, cross-team risk detection models. This includes everything from feature pipeline design to model selection (e.g., GNNs, LSTMs, LLMs) and high-performance serving.
Drive the Technical Roadmap: You will be the primary technical voice influencing at an organizational level. Work with Product, Ops, and other stakeholders to translate ambiguous business needs into a clear technical roadmap. You will be the primary technical voice defining the "how."
Mentor and Lead: Act as a senior technical leader and mentor for other senior (IC5), mid-level, and junior engineers on the team.
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.
Build Context-Aware Risk Systems: Architect the adaptive logic that decides which friction (a quiz, an LLM agent, a human review) to apply to which user, balancing security with user experience.