Staff Software Engineer, Faire

• An experienced technical leader with substantial backend architecture and developer tooling experience to work alongside our team, develop and deliver on a technical roadmap for how code gets written and shipped at Faire, and engage with engineering teams organization-wide
• Extensive experience designing, building, and running distributed backend systems in production, including finding and permanently fixing problems at scale
• Experience owning the design of developer tools, CI/CD systems, or internal platforms that other engineering teams build on and hundreds of engineers use, and making design decisions that span several teams
• Hands-on experience building on large language models and coding agents: running agents, giving them tools, checking their output, and tracking what they do and what they cost. You use AI coding tools every day
• Experience running services with uptime targets: defining SLAs, building monitoring and alerting, and owning incidents for systems other developers depend on
• Good judgment on performance and cost tradeoffs, with a record of making a system cheaper to run without making it worse
• A record of mentoring engineers and raising the level of the teams you have worked on, both on your immediate team and across the organization
• Strong communication skills; ability to bring disparate groups of people to a shared understanding
• A bachelor's degree in Computer Science/Software Engineering or equivalent industry experience

Desirable

• Our Engineering organization owns the software that makes our marketplace work
• Our Engineering Platform group enables product engineering teams to build and operate software with unmatched speed and quality
• The Code Authoring team owns the developer environment at Faire: the AI coding tools engineers use every day, remote development environments, and the sandboxes our end-to-end tests run in
• We care about developer experience, the quality of code written by people and agents, the reliability of the tools engineers depend on, and the cost of running those tools
• ---The team focuses on the following areas and capabilities:
• AI code authoring harnesses
• Cloud environments for running agents on Claude and Cursor
• Validation of agent-written code: CI checks, pre-push checks, and automated review
• Agent memory and context management
• Model selection and LLM cost per pull request
• Remote development environments
• Sandboxes for end-to-end testing and agent runs
• Pre-push validation speed
• Tracing and cost visibility for every agent run
• Uptime and SLAs for developer tooling
• Developer cloud infrastructure cost across AWS and OCI
• Integrations with Linear and GitHub
• The team also evaluates AI coding tools and owns our relationships with those vendors
• Own the architecture of the systems that run coding agents against our codebase: the service that turns tickets into pull requests, the sandboxes and remote development environments it runs in, and the integrations with Linear, GitHub, Claude, and Cursor. You decide how these pieces fit together and hold the long-term design
• Own the developer workflow end to end: how an engineer or an agent gets from a ticket to a merged, tested change. Know where it is slow or fragile, and change the tooling to fix it
• Run these systems as production infrastructure, with clear ownership, monitoring, uptime targets, and accountability when they fail. Developers across Faire depend on them every day
• Own what the platform costs to run: know what each agent run and each development environment costs, and keep that spend proportional to what it delivers
• Set the standard for how teams build: design reviews, testing, operational readiness, and the quality bar for code written by people and by agents
• Represent the pod to the rest of engineering: partner with other teams on the workflows they own, take part in planning and prioritization, and shape the roadmap around what the tooling can and cannot yet do
• Mentor engineers on the pod and across the organization
• ---Technologies we use & teach---
• (Experience in similar technologies is welcomed)
• Backend: Kotlin, Java, JUnit, Hibernate, Guice, Jersey, MySQL, CockroachDB, AWS
• Frontend: React, TypeScript/JavaScript
• Infrastructure & Data: Python, HTTP, JSON, Protocol Buffers, experimentation frameworks, analytics tooling