Senior Machine Learning Engineer, Coinbase

• 6-8+ 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 designing and deploying large-scale 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)
• 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

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
• 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 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: 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 technical leader and mentor for mid-level and junior engineers on the team. You will lead by example through code reviews, design docs, and coaching
• 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