Machine Learning Engineer II

About The Company Affirm is a pioneering financial technology company dedicated to reinventing credit to make it more honest, transparent, and user-friendly. Our mission is to give consumers the flexibility to buy now and pay later without hidden fees or compounding interest, fostering financial empowerment and trust. As a remote-first organization, Affirm leverages innovative technology and data-driven solutions to provide seamless and responsible lending experiences. We are committed to creating an inclusive environment that values diversity, transparency, and continuous growth, ensuring our team members are supported and empowered to make a meaningful impact in the financial industry. About The Role We are seeking a talented Machine Learning Engineer to join our Underwriting ML team. In this role, you will be instrumental in developing and enhancing machine learning systems that make real-time transaction decisions. Your primary focus will be on assessing repayment risks and calculating the expected value of each Affirm checkout. Working closely with experienced ML engineers, platform partners, and cross-functional stakeholders, you will take models from initial concept through to deployment and ongoing monitoring. Your work will directly influence our ability to offer responsible credit solutions while maintaining operational robustness and scalability. This position offers an exciting opportunity to innovate within a fast-paced environment, utilizing cutting-edge ML techniques and data pipelines. You will contribute to building scalable feature pipelines, prototype new modeling ideas, and ensure the health and performance of production models. Your efforts will help us deliver a reliable, efficient, and transparent credit experience to millions of consumers across Canada and beyond. Qualifications Minimum 2+ years of experience as a machine learning engineer or a PhD in a relevant field. Proficiency in Python programming and experience writing production-quality code. Experience building and evaluating classification models, preferably gradient-boosted decision trees such as LightGBM, XGBoost, or CatBoost. Hands-on experience with deep learning frameworks, particularly PyTorch. Knowledge of distributed data processing frameworks like Spark; familiarity with Ray, Dask, or similar tools is a plus. Experience with ML lifecycle tools such as Kubeflow, Airflow, MLflow, or equivalent platforms. Proficiency with AI-powered developer tools to accelerate development, debugging, and code quality. Strong problem-solving skills with the ability to translate business scenarios into technical solutions. Experience navigating large codebases, debugging, and conducting code reviews. Demonstrated ownership of personal growth through proactive feedback and continuous learning. Excellent verbal and written communication skills for effective collaboration across diverse teams. Relevant practical experience or a Bachelor’s degree in Computer Science, Data Science, or related fields. Responsibilities Develop, iterate, and optimize machine learning models for underwriting prediction, utilizing a mix of approaches for tabular and sequential data. Build and scale feature pipelines and training datasets from proprietary and third-party signals, collaborating with data and platform teams as needed. Prototype new modeling ideas, conduct offline experiments, and drive successful approaches into production with appropriate risk controls. Integrate models into batch and real-time decision systems, ensuring operational reliability, low latency, and scalability. Monitor model and data health, establish retraining and backtesting workflows, and implement continuous improvements. Collaborate with engineering, risk analytics, product, and ML platform teams to define requirements, evaluate tradeoffs, and communicate results effectively. Maintain documentation, conduct code reviews, and contribute to a culture of high-quality, maintainable code. Benefits Comprehensive health care coverage, including premiums paid for you and your dependents. Flexible Spending Wallets with stipends for technology, food, lifestyle needs, and family expenses. Generous vacation and holiday policies to support work-life balance. Participation in the Employee Stock Purchase Plan (ESPP) with discounts on company shares. Opportunities for professional development, remote work flexibility, and a collaborative team environment. Equal Opportunity Affirm is committed to fostering an inclusive environment and is proud to be an equal opportunity employer. We provide reasonable accommodations to candidates with disabilities during the hiring process. We do not discriminate based on race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. Our goal is to ensure that all applicants and employees feel valued, respected, and empowered to contribute their unique perspectives and talents.

Develop and optimize machine learning models for real-time underwriting decisions and repayment risk assessment. Build scalable feature pipelines and integrate models into batch and real-time systems while ensuring operational reliability.

Requires at least 2 years of ML engineering experience or a PhD, with proficiency in Python and deep learning frameworks like PyTorch. Candidates must have experience with gradient-boosted decision trees and ML lifecycle tools such as Kubeflow or Airflow.

• Comprehensive health care coverage • Flexible Spending Wallets • Generous vacation and holiday policies • Employee Stock Purchase Plan (ESPP) • Professional development • Remote work flexibility

• Machine learningPreferred
• PythonPreferred

Identified from the job description. Confirm important requirements above.

• Python
• Machine Learning
• LightGBM
• XGBoost
• CatBoost
• PyTorch
• Spark
• Kubeflow
• Airflow
• MLflow
• Distributed Data Processing
• Classification Models
• Deep Learning
• Feature Pipelines
• Model Monitoring
• Code Review

• Technology
• Data & Analytics
• Engineering
• Software
• Finance & Accounting

Minimum education

Bachelor’s degree

Minimum experience

2+ years

Posting language

English

Working hours

40 hours per week

Seniority

Associate