Affirm is seeking a Staff Machine Learning Engineer to optimize loan flows across their properties by shaping the technical strategy for portfolio management systems. This role aims to balance unit economics, enhance user experience, and drive product growth through advanced ML solutions.
Requirements
- Proficiency in machine learning techniques such as Generalized Linear Models, Gradient Boosting, Deep Learning, and Probabilistic Calibration
- Strong programming skills in Python and data manipulation expertise in SQL
- Experience delivering major features or system components, including deprecation of existing functionalities
- Ability to write high-quality, maintainable, and scalable code
- Comfortable working across all levels of system architecture, from low-level idioms to large system design
- Domain knowledge in credit risk, portfolio management, learning to rank, and personalization (preferred)
Responsibilities
- Define and execute the technical strategy for the ML portfolio, aligning with business goals and product roadmaps
- Develop, optimize, and maintain machine learning models and tools for portfolio management and decisioning
- Architect scalable and reliable ML systems that support real-time and offline decision-making processes
- Implement monitoring, alerting, and operational procedures to ensure system availability and performance
- Lead code reviews, establish design standards, and promote best practices within the engineering team
- Drive innovation by exploring new ML techniques, tools, and methodologies to improve portfolio management capabilities
- Document technical solutions and share knowledge through writing, presentations, and tech talks
Other
- 8+ years of industry experience in machine learning, data engineering, or related roles
- Excellent verbal and written communication skills for effective cross-functional collaboration
- Proven track record of gathering and iterating on feedback to improve technical solutions and team performance
- Act as a technical mentor and leader, providing guidance and feedback to team members to foster growth and excellence
- Remote-first work environment, providing flexibility to work from almost anywhere within the United States
