Software Engineer, AI Team

About Sprig

Sprig is the customer survey platform, rebuilt around AI agents.

For two decades, teams have relied on Qualtrics, Medallia, and Survey Monkey to understand their customers. These platforms were built for a slower era, with manual setup, fragmented workflows, and insights that arrive too late to share decisions. The gap between how fast products move and how fast research can keep up has never been wider.

We're building something different. With Sprig, AI agents design studies, adapt surveys in real time, and turn responses into clear, actionable insight. Teams move from question to evidence in hours instead of weeks, without sacrificing rigor. Product teams no longer guess. They know.

Our mission is to make deep customer understanding effortless and always on, so the best teams in the world can build products their customers love.

Companies like Microsoft, Door Dash, Notion, Figma, Coinbase, and Trip Advisor use Sprig to stay close to their users. Backed by 85M from Andreessen Horowitz, Accel, and First Round Capital, we're growing quickly across the world's leading product organizations.

The category is being rebuilt, with AI at the core. If you're excited by hard problems and the chance to define how modern teams understand their customers, we'd love to meet you.

Sprig's AI team builds the systems that help user-experience researchers and product managers understand their users ry 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.

• Build and ship features – Develop backend APIs and AI flows that help product and research teams understand user experiences at scale.

• Own small features end-to-end – Take responsibility for well-defined product features, from design and implementation to monitoring and iteration.

• Collaborate and learn from experienced engineers – Work closely with senior teammates to review designs, debug complex issues, and improve your technical judgment.

• 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.

• Contribute to quality and shared understanding – Write reliable, testable code, improve observability, and document designs so others can build confidently on your work.

• Experience – You have 2+ years of professional experience building and shipping production software.

• 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 or ready to switch to Type Script – You'll use Type Script for most development. Familiarity with technologies like Node.js, Temporal, AWS, or Postgre
  
  SQL is a plus. If your main experience is in python or other languages, it is ok if you're ready to switch to working primarily in Type Script.

• Fundamentals – You have a solid understanding of data structures, RESTful APIs, SQL, and testing.

• Communicator – You communicate clearly, contribute to reviews and design discussions, and learn through feedback.

• Growth minded – You are motivated to extend skills into distributed AI workflows, inference systems, and prompt and context engineering.