Software Engineer (Backend, Machine Learning Training & Serving)

About this role

• Machine Learning is central to how Affirm delivers on its mission, powering decisions across underwriting, fraud, servicing, and personalization
• We are seeking a Software Engineer II to join our ML Training & Serving engineering team, the team that builds and operates the critical infrastructure that enables models to be trained and served across Affirm
• This role sits at the intersection of distributed systems, ML infrastructure, and platform engineering
• We value engineers who listen closely, invite feedback, and work effectively across organizational boundaries, while maintaining a high bar for ownership, technical judgment, and execution
• Strong candidates care about building reliable shared platforms, improving developer velocity, and creating durable systems that enable teams across the company to move faster with confidence
• With the support of your team’s tech lead and manager, you will break down larger projects into individual tasks, deliver them in multiple phases, and collaborate with others to ensure timely delivery of your work
• You will support your peers and stakeholders in the product development lifecycle by collaborating across engineering, articulating technical constraints, and partnering on decisions that properly consider risks and trade-offs
• You will support the operations and availability of your team’s artifacts by creating and monitoring metrics, escalating when needed, and supporting “keep the lights on” & on-call efforts
• You will contribute to a sense of community on your team by engaging in growth and development activities such as participation in the interview process

### Benefits

• Compensation: We have a simple, flexible, and transparent remote-first compensation structure so you can make the best decisions for yourself and your family
• Spending Wallets: Access tech, food, lifestyle, and family planning wallets for your expenses
• Supportive Communities: Get involved with our employee resource groups and community gr...