Staff Machine Learning Engineer - AI/ML Risk Platform - Remote

Overview

As a Staff Machine Learning Engineer at Coinbase, you will play a critical role in building sophisticated AI/ML models to protect users and prevent fraud on the platform.

In Short

• Lead the design and architecture of complex risk detection models.
• Own a critical risk domain, driving technical strategy and execution.
• Mentor and lead junior and mid-level engineers.
• Apply advanced machine learning methodologies to solve complex challenges.
• Collaborate with cross-functional teams to enhance risk detection capabilities.
• Utilize cutting-edge ML platforms for model deployment and iteration.
• Influence technical roadmaps and communicate effectively with stakeholders.
• Implement context-aware risk systems balancing security and user experience.
• Contribute to a culture of feedback and continuous improvement.
• Engage in a fast-paced, high-caliber work environment.

Requirements

• 8-10+ years of experience in software engineering and AI/ML.
• Proven technical leadership in deploying large-scale ML systems.
• Deep expertise in applied AI/ML techniques, especially in risk domains.
• Expert-level coding skills in Python and familiarity with ML frameworks.
• Strong collaboration and communication skills.
• Commitment to building an open financial system.
• Experience with modern data and AI/ML infrastructure is a plus.
• Master’s/Ph.D. in a relevant field is a nice to have.
• Understanding of MLOps best practices.
• Passion for protecting users from fraud and scams.

Benefits

• Competitive salary with bonus and equity eligibility.
• Comprehensive medical, dental, and vision benefits.
• 401(k) plan to support your financial future.
• Dynamic and collaborative work culture.
• Opportunities for professional growth and development.
• Engagement in company-wide offsites and team-building activities.
• Support for work-life balance and flexible working arrangements.
• Commitment to diversity and inclusion in the workplace.
• Access to cutting-edge tools and technologies.
• Encouragement of curiosity and innovation in problem-solving.