Senior Backend Software Engineer (AI Platform)

• Affirm’s engineering team is building large-scale, fault-tolerant, global infrastructure shared across multiple financial products, merchants, and vendors. Ensuring that this infrastructure is transparently available to all engineers is critical to the business. The engineering culture emphasizes blameless postmortems, detailed technical specifications, and gathering feedback before large system changes
• The AI Platform team owns Affirm’s paved path for building, running, evaluating, and governing AI systems. Its customers include Affirm users, engineers, and production workloads. The team builds and exposes simple, governed interfaces to models, tools, and agents, with identity, cost attribution, reliability, and security built in. Components include the LLM Gateway, MCP Gateway, Agent Runtime, and evaluations and observability layer. The gateway is in daily use across engineering, the first production agent is live, and the team is expanding from developer productivity to production inference and agents
• Plan, execute, and own technical decisions and projects within the AI Platform team, from design through launch and on-call
• Design and build gateway, runtime, and evaluation services that take AI workloads from pilot to production, securely scaling across millions of users and their transactions
• Build identity, quota, routing, caching, and failover controls to make AI spending attributable and governable and help teams adopt new models and tools
• Partner with security, infrastructure, and product engineering teams to set guardrails for agentic workloads and the on-behalf-of identity model for tool access
• Create and monitor metrics for the team’s systems, and own the operational health of what you ship, including monitoring, alerting, runbooks, and on-call
• Set and improve code review and design standards, and advocate for them beyond your team through writing and tech talks
• Use AI tools and agentic workflows daily to multiply your own code, design review, and operations, and push the team on what a small AI-native team can deliver
• Educate, mentor, and grow engineers on the team, and drive best practices around design, coding, and testing

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 groups
• Remote-first Workforce: If your role is remote, you can set up shop anywhere in your home country
• Generous Time Off: Take the time you need when life happens
• Health Benefits: Get a plan that fits your needs
• Mental Healthcare: Take care of your mind with great mental health programs
• Parental Leave: Birth and non-birth parents get 18 weeks’ paid leave. Plus, a 4-week return-to-work transition program, at full base pay
• Away Days: We offer 20 company-wide paid days off—which help our teams collectively pause to recharge
• Learning & Development: Engage in exciting learning programs to level up your growth

• A track record of taking ownership of your growth and proactively seeking feedback from your team, manager, and stakeholders
• Proficiency making significant changes in a large codebase, and experience developing tools and practices that enable you and your team to do so safely
• Equivalent practical experience or a bachelor’s degree in a related field
• Practical experience building with LLMs, agents, or AI frameworks in production, such as inference APIs, LLM gateways (LiteLLM or similar), MCP, tool calling, evaluations, or agent runtimes
• Strong verbal and written communication skills to support effective collaboration with a global engineering team
• Ability to write high-quality code that is easily understood and used by others
• Experience defining a technical plan for delivering a significant feature or system component with an elegant, simple, and extensible design
• An understanding of AI usage and the infrastructure needed to support and leverage AI workloads across different disciplines
• A track record of developing highly available distributed systems using technologies such as AWS, MySQL, and Kubernetes
• 4+ years of experience designing, developing, and launching backend systems at scale using languages such as Python or Kotlin