Staff Software Engineer, AI

As a Staff Software Engineer, AI, you’ll help define the technical direction of the AI-powered product and platform—designing and building the software systems that enable highly reliable, production-grade AI agents in complex enterprise environments.This is a hands-on Staff-level engineering role for someone who combines exceptional software engineering fundamentals, deep AI expertise, strong product instincts, and technical leadership. You’ll work across the stack—from distributed backend systems and AI infrastructure to agent orchestration and customer-facing product experiences—while influencing architecture and engineering standards across the organization.Location: San Francisco, CA — HybridCompensation: Up to $310,000 base + equityWhat You’ll OwnArchitect & Build AI-Native Software SystemsDesign and build scalable software architecture supporting AI-powered products and agentic workflowsSet technical direction for critical components across the AI application and platform stackBuild reliable distributed systems capable of supporting complex, enterprise-grade AI workloadsIdentify architectural and engineering bottlenecks impacting reliability, scalability, and development velocityMake high-leverage technical decisions and remain hands-on through implementation and productionBuild & Ship AI ProductsOwn complex product areas end-to-end—from architecture and implementation through deployment and iterationBuild agent orchestration, retrieval, workflow, and reasoning systems capable of handling real-world enterprise complexityDevelop the infrastructure surrounding LLMs, including model integrations, prompts, tools, guardrails, evaluations, observability, and feedback loopsBuild APIs, backend services, data pipelines, and platform capabilities that power AI-native product experiencesRapidly prototype new approaches, validate them with real users, and harden successful solutions for productionDebug difficult issues spanning application code, AI behavior, infrastructure, and distributed systemsDrive Engineering Standards & Technical DirectionPartner with Engineering, Product, and Design leadership to translate product strategy into scalable technical architectureEstablish engineering patterns and standards for building reliable AI-powered softwareCreate reusable platforms, abstractions, APIs, and tooling that increase engineering velocity across the organizationInfluence technical decisions across multiple teams and product areasMentor senior engineers and raise the technical bar through architecture reviews, code reviews, and hands-on collaborationBalance long-term architectural quality with the speed required to ship and learnAdvance the AI PlatformStay at the frontier of LLMs, agents, applied AI, and AI-native software developmentRapidly evaluate new models, frameworks, infrastructure, and techniques for practical production useImprove how AI systems are evaluated, monitored, debugged, and operated in productionDevelop internal knowledge and best practices around building reliable AI applicationsHelp establish the organization as a technical leader in enterprise and vertical AIWho You AreYou’re a Staff-level software engineer who happens to be deeply experienced in AI—not an AI researcher who occasionally writes production code.These principles resonate with you:Software engineer first: You have exceptional engineering fundamentals and know how to build maintainable, scalable production systemsDeep AI fluency: You understand how modern LLM and agent systems work and can translate rapidly evolving AI capabilities into reliable productsTechnical leadership: You influence architecture and engineering direction through expertise and credibility rather than authoritySystems thinker: You understand second-order effects and design beyond the immediate featureProduct-minded: You care about whether customers actually receive value, not simply whether the technology worksMultiplier: Your architecture, tooling, mentorship, and technical decisions make other engineers more effectivePrincipled pragmatism: You know when sophisticated engineering is warranted and when the right answer is simply to shipEnd-to-end ownership: You take responsibility for outcomes—from initial architecture through production reliabilityExperienceWe care more about capability and trajectory than checking every box, but strong candidates will typically bring:8+ years of production software engineering experience, with significant experience building AI/ML-powered products or platformsStaff-level experience owning architecture and complex technical systems across multiple teams or product areasDeep expertise in TypeScript, Python, backend engineering, APIs, and distributed systemsProven experience designing and shipping LLM-powered applications into productionExperience building agentic systems, orchestration layers, tool-calling workflows, and multi-step AI applicationsStrong knowledge of RAG, retrieval architectures, vector databases, embeddings, and data pipelinesHands-on experience with modern LLM APIs and ecosystems such as OpenAI, Anthropic, Gemini, LangGraph, or similar technologiesExperience designing evaluation frameworks, observability systems, guardrails, and reliability infrastructure for AI applicationsStrong understanding of traditional software reliability alongside the unique failure modes introduced by probabilistic AI systemsExperience designing scalable services and systems for enterprise customersTrack record of influencing technical direction and mentoring experienced engineersWhat Should Excite YouAI-native product engineering: Building products where AI is fundamental to the architecture rather than an added featureEnterprise-grade reliability: Turning probabilistic AI capabilities into software professionals can depend onAgentic systems: Building agents capable of reasoning, retrieving information, using tools, and completing complex workflowsHuman-in-the-loop systems: Determining where automation creates leverage and where expert judgment should remain involvedNuanced evaluation: Measuring quality when there isn’t always a single objectively correct answerExplainability: Making AI behavior transparent, debuggable, and trustworthyComplex domains: Building elegant software for environments involving compliance, security, and enterprise rigorShipping real value: Moving quickly from prototype to production and building AI experiences customers actively rely onBenefitsComprehensive health and wellness benefitsFlexible time off and work schedulesTechnology reimbursements401(k) planTwice-yearly in-person offsites across the U.S.Wellness benefits starting on your first day