Affirm's engineering team is building a large-scale, fault-tolerant, and global infrastructure that is shared across multiple financial products, merchants, and vendors. Ensuring that our infrastructure is transparently available to all engineers is critical to the success of the business. We pride ourselves on our culture across engineering of writing blameless postmortems as well as writing detailed tech specs and gathering feedback before large changes to systems.

The AI Platform team owns Affirm's paved path for building, running, evaluating, and governing AI systems. Our customers are not only Affirm users, but also every engineer and a growing set of production workloads at Affirm. As a team, we are responsible for building and exposing simple, governed interfaces to models, tools, and agents, with identity, cost attribution, reliability, and security built in. Examples of components we own include the LLM Gateway that fronts every approved model (Claude, ChatGPT, and open-weight models across AWS Bedrock and other providers), the MCP Gateway that mediates and audits tool calls, the Agent Runtime that gives agentic workloads bounded execution, and the evals and observability layer that tells teams whether an AI change is better, cheaper, or safe to ship. We are early: the gateway is in daily use across engineering, the first production agent is live, and the next year takes us from developer productivity to production inference and agents.


What You'll Do

• Plan, execute, and own technical decisions and projects within the AI Platform team, from design through launch and on-call.
• Design and build the gateway, runtime, and evaluation services that take AI workloads at Affirm from pilot to production, securely scaling across millions of users and their transactions.
• Make AI spend attributable and governable: build the identity, quota, routing, caching, and failover controls that let teams adopt new models and tools without re-solving the same platform problems.
• Partner with security, infrastructure, and product engineering teams to set the guardrails for agentic workloads and the on-behalf-of identity model for tool access.
• Create and monitor metrics for your team's systems, and own the operational health of what you ship: 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 every day as a multiplier for 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 in the team, and drive best practices around design, coding, and testing.

What We Look For

• You have 4+ years of experience designing, developing and launching backend systems at scale using languages like Python or Kotlin.
• You have a track record of developing highly available distributed systems using technologies like AWS, MySQL and Kubernetes.
• You have practical experience building with LLMs, agents, or AI frameworks in production: inference APIs, LLM gateways (LiteLLM or similar), MCP, tool calling, evals, or agent runtimes.
• You understand AI usage and the infrastructure needed to support and leverage AI workloads across different disciplines.
• You have experience defining a technical plan for the delivery of a significant feature or system component with an elegant, simple and extensible design. You write high quality code that is easily understood and used by others.
• You are proficient at making significant changes in a large code base, and have developed a suite of tools and practices that enable you and your team to do so safely.
• Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.
• You have strong verbal and written communication skills that support effective collaboration with our global engineering team.
• This position requires either equivalent practical experience or a Bachelor’s degree in a related field.
  
  

Pay Grade - N
Equity Grade - 9

Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.

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: $195,000 - 255,000

USA base pay range (all other U.S. states) per year: $173,000-233,000

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