Manager, Machine Learning Engineering
AI & Machine Learning
Excel
WFA Digital Insight
As the demand for skilled machine learning engineers continues to soar, with a 25% increase in job postings over the past year, Affirm stands out as a pioneer in reinventing credit and making it more honest and friendly. With the rise of digital payments, the need for robust fraud detection systems has never been more critical. This role offers a unique chance to lead a team of ML engineers and drive the evolution of modeling approaches, including representation learning and transformer-based techniques. Candidates should be prepared to showcase their technical expertise, leadership skills, and ability to thrive in a fast-evolving environment.
Job Description
About the Role
The Manager, Machine Learning Engineering at Affirm is a crucial role that involves leading a team of machine learning engineers to develop and improve models that detect and prevent fraud in a rapidly evolving environment. As a key member of the Fraud Machine Learning team, you will be responsible for defining the technical and modeling strategy for fraud detection, guiding the team across the full machine learning lifecycle, and collaborating closely with cross-functional teams to ensure high-quality models are effectively integrated into decisioning systems.The ideal candidate will have a strong technical background, excellent leadership skills, and the ability to drive the evolution of modeling approaches, including the adoption of representation learning and transformer-based techniques. You will be working in a dynamic environment where no two days are the same, and the ability to adapt and innovate is essential.
What You Will Do
- Set the technical and modeling strategy for fraud detection, aligning team efforts with key business outcomes such as fraud loss reduction, approval rates, and customer experience
- Lead a team of machine learning engineers to design, build, and iterate on high-impact fraud models across the full ML lifecycle, from experimentation to production
- 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 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 identify business problems and develop machine learning solutions that meet those needs
- Design and implement A/B testing and experimentation to measure the impact of machine learning models
- Stay up-to-date with industry trends and advancements in machine learning and apply that knowledge to improve the team's capabilities
- Participate in code reviews and ensure that all code meets the team's standards for quality and maintainability
- Develop and maintain technical documentation for all models and systems
What We Are Looking For
- Bachelor’s degree in a technical field 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
- Excellent communication and leadership skills, with the ability to articulate complex technical concepts to non-technical stakeholders
- Proficiency in tools such as Excel for data analysis and reporting
Nice to Have
- Experience working with cloud-based technologies such as AWS or GCP
- Knowledge of containerization using Docker and orchestration using Kubernetes
- Familiarity with agile development methodologies and version control systems such as Git
- Experience with machine learning frameworks such as TensorFlow or PyTorch
Benefits and Perks
- Competitive salary and bonus structure
- Equity compensation package
- Comprehensive health, dental, and vision insurance
- Flexible PTO policy and remote work options
- Access to professional development opportunities and training programs
- Monthly stipends for health, wellness, and tech spending
- 100% subsidized medical coverage, dental, and vision for you and your dependents
- Opportunity to work with a talented and diverse team of professionals
- Collaborative and dynamic work environment
- Recognition and reward for outstanding performance and contributions
How to Stand Out
- Develop a strong understanding of machine learning principles and techniques, including representation learning and transformer-based models.
- Showcase your ability to lead and manage teams, with a focus on technical excellence and ownership.
- Highlight your experience working with cross-functional teams, including product, analytics, and engineering.
- Be prepared to articulate complex technical concepts to non-technical stakeholders, and demonstrate your ability to communicate effectively.
- Emphasize your experience working with scalable systems and data pipelines, and your ability to drive the evolution of modeling approaches.
- Prepare to discuss your experience with tools such as Excel, and your proficiency in data analysis and reporting.
- Be ready to provide specific examples of your accomplishments and the impact you've made in previous roles.
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