Manager, Machine Learning Engineering

About the Role

In this context, the ability to collaborate closely with various teams, including Product, Fraud Analytics, Risk, and Platform, is essential. The goal is to ensure that high-quality models are effectively integrated into decisioning systems, contributing to key business outcomes such as fraud loss reduction, approval rates, and customer experience enhancement.

The machine learning engineering team operates in a rapidly evolving environment, where fraud patterns and consumer behaviors are continuously changing. Thus, the ability to adapt, innovate, and drive the evolution of modeling approaches is crucial. This includes embracing advanced techniques like representation learning and transformer-based models to better capture complex behavioral patterns.

What You Will Do

• Set the technical and modeling strategy for fraud detection, aligning team efforts with key business outcomes.
• Lead a team of machine learning engineers in designing, building, and iterating on high-impact fraud models across the full ML lifecycle.
• Drive the evolution of modeling approaches, including the adoption of representation learning, transformer-based methods, and other advanced techniques for modeling complex behavioral data.
• Partner cross-functionally with Product, Fraud Analytics, Risk, and Engineering teams to define solutions, evaluate trade-offs, and ensure models are effectively integrated into decisioning systems.
• Develop talent by coaching engineers, providing feedback, and fostering a high-performing team culture grounded in technical excellence and ownership.
• Collaborate with stakeholders to prioritize projects and allocate resources effectively, ensuring alignment with business objectives.
• Stay updated with the latest advancements in machine learning and related fields, applying this knowledge to improve Affirm's fraud detection capabilities.
• Contribute to the development of the team's technical vision and strategy, ensuring it aligns with Affirm's overall mission and goals.
• Facilitate knowledge sharing and best practices across the engineering teams, promoting a culture of innovation and continuous learning.

What We Are Looking For

• Bachelor’s degree in a technical field (such as Computer Science, Mathematics, or Statistics) with 8+ years of industry experience, including 3+ years managing engineers.
• Experience with modern ML approaches, including representation learning, deep learning, or transformer-based models, as well as traditional methods such as gradient-boosted trees.
• Proven ability to lead teams delivering end-to-end ML solutions in production environments, including experimentation, evaluation, and model iteration in production.
• Strong engineering fundamentals and experience working with scalable systems and data pipelines.
• Track record of effective cross-functional collaboration with product, analytics, and engineering partners.
• Ability to operate in ambiguous, fast-evolving environments and drive clarity, prioritization, and execution.
• Proficiency in Excel and experience with data analysis and interpretation.
• Excellent communication and leadership skills, with the ability to motivate and guide a team of engineers.

Nice to Have

• Experience with cloud-based technologies and containerization (e.g., Docker).
• Familiarity with agile development methodologies and version control systems (e.g., Git).
• Knowledge of regulatory requirements and compliance issues related to consumer finance and fraud detection.
• Participation in open-source projects or personal projects that demonstrate machine learning expertise.

Benefits and Perks

• Competitive salary range (USA base pay range: $200,000 - $275,000 per year, depending on location and experience).
• Equity rewards as part of the total compensation package.
• Monthly stipends for health, wellness, and tech spending.
• 100% subsidized medical coverage, dental, and vision for you and your dependents.
• Opportunities for professional growth and development in a rapidly expanding company.
• Flexible working hours and remote work arrangements.
• Access to cutting-edge technologies and tools.
• Collaboration with a talented team of professionals who are passionate about making a positive impact in consumer finance.

• Highlight your experience with machine learning frameworks and technologies, especially those relevant to fraud detection and prevention.
• Prepare examples of successful projects where you've led the development and implementation of machine learning models in production environments.
• Emphasize your ability to communicate complex technical concepts to both technical and non-technical stakeholders, as this is crucial for cross-functional collaboration.
• Show a deep understanding of the latest advancements in machine learning, including representation learning and transformer-based models, and how these can be applied to improve fraud detection.
• Be ready to discuss your approach to talent development and team leadership, including how you foster a culture of innovation, technical excellence, and ownership among your team members.
• Research Affirm's mission and values, and be prepared to explain how your skills and experience align with the company's goals and culture.
• Prepare questions to ask during the interview, such as what the biggest challenges facing the team are, how success is measured, and what opportunities there are for professional growth and development.