Software Engineer, Backend; Capacity Modeling

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.

The Capacity Modeling team ensures that Affirm can safely handle forecasted traffic—especially during peak sales events—by translating demand forecasts into concrete, testable capacity plans. They build and maintain statistical capacity models that map traffic inputs (e.g., checkout volume, app opens, batch/job activity) to resource outputs (CPU, memory, connections) and then convert those forecasts into recommended capacity settings such as pod counts, HPA ranges, and/or infrastructure sizing changes across regions and environments.

The coverage of infrastructure resources includes not only compute but also infrastructure such as Elastic Cache, DynamoDB, AuroraDB, and more.

• Set technical strategy for the team on a year‑long time scale, linking it to critical, business‑impacting projects.
• Collaborate across teams in the product development lifecycle with product management, design, and analytics to ensure technical sustainability, risks, and trade‑offs are well understood and managed.
• Act as a force‑multiplier for the team through definition and advocacy of technical solutions and operational processes.
• Own the team’s operations and availability by ensuring the right monitoring, triage rotations, playbooks, policies, testing, and alerting are in place to support on‑call efforts.
• Foster a culture of quality and ownership by setting code review and design standards for the team and advocating for them beyond the team through writing and tech talks.
• Help develop talent on the team by providing feedback and guidance, and lead by example.

• 8+ years of experience designing, developing, and launching backend systems at scale using languages such as Python or Kotlin.
• Extensive track record of developing highly available distributed systems using technologies such as AWS, MySQL, Spark, and Kubernetes.
• Experience supporting scale‑up and scale‑down during large‑scale sales events such as Prime Day, Black Friday, Cyber Monday, or other sales holidays.
• Experience tuning autoscaling, CPU/memory, and multi‑threading for compute‑type workloads.
• Experience delivering major features, system components, or deprecating existing functionality through a defined technical and execution plan, writing high‑quality code that is easily understood and used by others.
• Thrives in ambiguity and is comfortable moving from low‑level language idioms to the architecture of large systems to understand how they work.
• Demonstrates mastery of gathering and iterating on feedback from engineering and cross‑functional peers.
• Strong verbal and written communication skills that support effective collaboration with the global engineering team.
• This position requires either equivalent practical experience or a Bachelor’s degree in a related field.

Base Pay Grade: P
Equity Grade: 13

Employees new to Affirm typically come in at the start of the pay range. Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness, and tech spending, and benefits (including 100% subsidized medical coverage, dental, and vision for you and your dependents).

USA base pay range (CA, WA, NY, NJ, CT) per year: $225,000 - $275,000
USA base pay range (all other U.S. states) per year: $200,000 - $250,000

• Health care coverage—Affirm covers all premiums for all levels of coverage for you and your dependents.
• Flexible Spending Wallets—generous stipends for spending on technology, food, various lifestyle needs, and family‑forming expenses.
• Time off—competitive vacation and holiday schedules allowing you to take time off to rest and recharge.
• ESPP—An employee stock purchase plan enabling you to buy shares of Affim at a discount.

We believe it’s on us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. For U.S. positions that could be performed in Los Angeles or San Francisco, pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative, we will consider qualified applicants with arrest and conviction records.