Machine Learning Engineer II

About the Role

As part of the Servicing ML team, the engineer will be at the forefront of developing innovative AI solutions that enhance the customer experience. This role requires a deep understanding of machine learning principles, software development, and the ability to collaborate effectively in a remote work environment. The engineer will also contribute to the development of evidence extraction pipelines, leveraging large language models to process unstructured data into actionable insights.

The company's commitment to reinventing credit to make it more honest and friendly provides a compelling context for this role. By joining Affirm, the Machine Learning Engineer II will be part of a team that is dedicated to making a positive impact on consumers' financial lives.

What You Will Do

• Develop AI systems that automate dispute and chargeback handling, utilizing structured evidence and business logic to improve the customer experience.
• Build models that automate refunds, ensuring customers receive their money back swiftly and efficiently.
• Design and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows, producing structured and actionable outputs.
• Prototype new modeling ideas, conduct offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
• Collaborate across Engineering, Servicing Operations, Product, and ML Platform teams to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical stakeholders.
• Work on the development of applications with LLM APIs, including structured extraction, prompt engineering, and orchestration frameworks.
• Contribute to the improvement of ML lifecycle tooling for training orchestration, experimentation, and model monitoring.
• Participate in code reviews, providing and receiving feedback to ensure high-quality, well-tested, and extensible code.
• Engage in continuous learning to stay updated with the latest advancements in machine learning and AI, applying this knowledge to improve existing systems and develop new solutions.

What We Are Looking For

• A minimum of 2+ years of experience as a machine learning engineer, with a strong background in Python and experience writing production-quality code.
• Experience building and evaluating models for tabular classification problems, preferably with gradient-boosted decision trees like LightGBM, XGBoost, or CatBoost.
• Familiarity with document and unstructured data processing, including PDF/image extraction, text parsing, or similar technologies.
• Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring, such as Kubeflow, Airflow, MLflow, or equivalent internal platforms.
• Proficiency in using AI-powered developer tools to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.
• Strong verbal and written communication skills that support effective collaboration with the global engineering team.
• Ability to take ownership of growth, proactively seeking feedback from the team, manager, and stakeholders.
• Experience with taking a simple problem or business scenario into a solution that interacts with multiple software components, executing on it by writing clear, easily understood, well-tested, and extensible code.

Nice to Have

• Experience with cloud-based platforms for deploying and managing machine learning models.
• Knowledge of Agile development methodologies and version control systems like Git.
• Participation in open-source projects or personal projects that demonstrate machine learning skills.

Benefits and Perks

• Competitive compensation package, including a base salary and potential for performance-based bonuses.
• Equity participation, offering a stake in the company's future success.
• Comprehensive health, dental, and vision insurance, with 100% coverage for employees and their dependents.
• Flexible remote work arrangements, with support for home office setup and periodic team meetups.
• Professional development opportunities, including training, conferences, and education assistance.
• Access to the latest tools and technologies, ensuring engineers have the best resources to excel in their roles.

• Tip 1: Ensure your resume and cover letter are tailored to highlight your machine learning engineering skills, especially in Python and experience with LLM APIs.
• Tip 2: Prepare to discuss your experience with model development, deployment, and monitoring, focusing on specific challenges you've overcome and lessons learned.
• Tip 3: Demonstrate your ability to communicate complex technical concepts to non-technical stakeholders, as this is a key aspect of collaboration in a cross-functional team.
• Tip 4: Be ready to walk through your code and design decisions during the interview process, showcasing your problem-solving skills and attention to detail.
• Tip 5: Research Affirm's products and mission, preparing thoughtful questions about the company's approach to fintech and how you can contribute to its goals.
• Tip 6: Consider creating a personal project or contributing to an open-source project to showcase your skills in machine learning and AI, especially if you're transitioning from a different field.
• Tip 7: Practice explaining your understanding of ethical considerations in AI development, as this is a critical aspect of working in fintech and consumer-facing technologies.