Senior Software Engineer, Backend (AI)

Sprig•San Francisco, CA

40d•$180,000 - $260,000•Hybrid

About The Position

Sprig is building the AI-native successor to legacy survey tools, like Qualtrics, Medallia, and SurveyMonkey. We believe the future of experience research won't be powered by slow, siloed platforms. It will be fast, intelligent, and deeply integrated into how modern teams build great products. Our mission is to make deep customer understanding effortless and always on. With Sprig, product teams no longer guess. They know. We are creating a future where AI uncovers insights, accelerates workflows, and enables teams to deliver exceptional customer experiences in real-time. Companies like Notion, Figma, Coinbase, and TripAdvisor already use Sprig to stay closer to their customers than ever before. We're scaling quickly toward $100M ARR, launching new AI-powered capabilities, and expanding our impact across the world's most innovative companies. If you're energized by bold ideas, rapid growth, and the opportunity to redefine an entire category, we'd love to meet you. About the Role Sprig's AI team builds the systems that help user-experience researchers and product managers understand their users at scale. Every day, we process over half a billion events and run millions of AI inference queries, transforming raw feedback and usage data into actionable product insights. As a senior software engineer, you'll own and evolve the backend systems that make this possible-distributed data pipelines, inference workflows, and the integrations that bring AI-powered understanding into Sprig's product. Our work spans from distributed data processing and inference pipelines to the product surface that UX researchers and PMs interact with. The team operates across the stack but this role emphasizes large-scale backend systems and integration with applied AI. You'll join a group that values pragmatic engineering, clear design, and measurable impact, advancing both our technical foundation and our ability to deliver trustworthy, privacy-safe analyses and insights. This is a hybrid role with three days working in our downtown San Francisco office, which is just a short walking distance from the Montgomery BART station.

Requirements

Experienced engineer - 5+ years of experience designing and maintaining scalable backend systems with a strong track record of delivering production-quality code.
Product-minded - Care deeply about usability and product outcomes, not just system correctness. You think about how your work enables better customer experiences.
Comfortable with TypeScript - You'll use TypeScript for most development. Familiarity with technologies like Node.js, Temporal, AWS, or PostgreSQL is a plus. Python experience is welcome if you're ready to work primarily in TypeScript.
Systems-oriented - Skilled at designing distributed, event-driven, and data-intensive systems that are reliable and performant.
Applied AI integration - Experienced in integrating external inference providers (e.g., OpenAI, Anthropic) for delivering product capabilities; model training experience not required.

Nice To Haves

Prompt and context engineering - Knowledge of prompt design and context handling; a strong plus.

Responsibilities

Deliver impactful systems end-to-end - Design, build, and ship backend features and distributed systems that power Sprig's AI-driven insights, taking projects from concept to production with sound engineering practices.
Shape Sprig's data and inference infrastructure - design, operate, and evolve distributed workflows for large-scale data processing and AI inference.
Extend across the stack - while the focus is backend, contribute where needed in the full stack, supporting product surfaces that bring AI analyses and insights to life.
Collaborate across teams and disciplines - work closely with product managers, designers, and other teams to define and deliver product capabilities.
Contribute to technical direction - Participate in design discussions and planning, helping the team make thoughtful trade-offs between speed, quality, and long-term scalability.
Raise the bar on engineering excellence - Champion reliability, performance, and maintainability across the AI stack while growing with the team and helping strengthen engineering practices across Sprig.