Staff Machine Learning Scientist

Summary

Join Marqeta as a Staff Machine Learning Scientist and lead the development of customer-facing, production-deployed machine learning capabilities for payments. You will bridge engineering and science, requiring expertise in ML development, MLOps, and AWS Sagemaker. This role involves collaborating with stakeholders, ensuring compliance, and aligning the technical vision with business needs. The position reports to the Sr. Director of Machine Learning and contributes to Marqeta's AI strategy. This flexible role can be performed remotely within the US or at the Oakland, CA headquarters. Compensation is tiered nationally, premium, and premium plus, based on location.

Requirements

• 6+ years experience in a data science, machine learning engineering, or similar role within the financial services, banking, or payments space
• 6+ years expertise building and operating large scale, customer-facing, production ML capabilities
• Strong understanding of ML techniques including supervised, unsupervised, and reinforcement learning
• Strong understanding of MLOps and the ML development lifecycle, particularly using AWS Sagemaker and related services
• Excellent communication and interpersonal skills, with the ability to cultivate relationships across teams, influence decision making, and collaborate with stakeholders at all levels of the organization
• A strong bias toward action and ability to operate proactively and effectively in a dynamic, fast-paced environment
• High ethical standards and a commitment to promoting a strong culture

Responsibilities

• Develop ML models and supporting technical capabilities across the end-to-end ML development lifecycle from ideation and prototyping through production deployment and monitoring
• Build ML capabilities that address critical internal and external customer-facing challenges in the payments space including risk, fraud, disputes processing, and named entity recognition
• Collaborate with key GTM, Product, and Engineering stakeholders to identify model development opportunities, guide decision making by leveraging payments expertise, and create prototypes to assess feasibility and expected business value
• Partner with legal, regulatory and risk stakeholders to ensure compliance of ML development efforts with all applicable legal and regulatory requirements
• Work together with Product and Technical Program Managers to align the technical vision for our ML ecosystem to the needs of our ML scientists

Preferred Qualifications

BS degree preferred

Benefits

• Multiple health insurance options
• Flexible time off – take what you need
• Retirement savings program with company contribution and after tax contributions
• Equity in a publicly-traded company and an Employee Stock Purchase Program
• Family-forming benefits, fertility support, and up to 20 weeks of Parental Leave
• Free therapy sessions, financial and professional coaching, and legal advice
• Monthly stipend to support our remote work model
• Annual “development dollars” to support our people growth and development