Senior Software Engineer, AI Team

Sprig•San Francisco, CA

4d•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. Sprig's AI engineering group builds the core technology that enables UX researchers and product leaders to understand customer behavior and feedback at massive scale. Our platform ingests and processes hundreds of millions of events daily and powers large volumes of AI-driven analysis to turn raw signals into clear, actionable insights for product teams. In this role, you'll be responsible for the backend systems that support this work—high-throughput data pipelines, AI inference orchestration, and the integrations that connect applied AI directly into Sprig's product experience. You'll help evolve the technical foundation that allows our customers to trust, scale, and operationalize AI-powered understanding. While the team collaborates across the full stack, this position is primarily focused on backend and distributed systems, with close proximity to applied AI infrastructure. You'll work alongside engineers who value thoughtful design, practical solutions, and shipping work that makes a measurable difference for customers—while maintaining strong standards around reliability, privacy, and correctness. This is a hybrid position requiring three days per week in our downtown San Francisco office, conveniently located near Montgomery BART.

Requirements

Strong backend engineering experience: 5+ years building and maintaining scalable backend systems, with a proven history of shipping robust, production-grade software.
Proficiency with TypeScript : Most development is done in TypeScript. Experience with Node.js, Temporal, AWS, or PostgreSQL is a plus. Python experience is welcome if you're comfortable working primarily in TypeScript.
Product-oriented mindset: You think beyond correctness and care about how systems enable intuitive, high-quality customer experiences.
Applied AI integration experience: Hands-on experience integrating third-party inference services (such as OpenAI or Anthropic) into real product workflows; model training is not required.
Distributed systems expertise : Comfortable designing event-driven, data-intensive architectures that operate reliably at scale.
Pragmatic execution style: You balance technical depth with speed, adapt quickly, and enjoy iterating in a fast-moving AI environment.

Nice To Haves

Prompt and context design knowledge : Familiarity with prompt construction and context management is a strong plus.

Responsibilities

Build and operate core backend systems: Design, implement, and deploy distributed services and workflows that underpin Sprig's AI-powered insights, owning projects from early design through production rollout.
Support product-facing experiences: While backend-focused, contribute across the stack as needed to enable the product features that surface AI analysis to researchers and PMs.
Advance data and inference workflows : Develop and maintain scalable pipelines for large-scale data processing and AI inference, ensuring performance, reliability, and operational clarity.
Influence technical decisions: Participate in system design and planning discussions, helping balance iteration speed with long-term system health and scalability.
Partner cross-functionally: Collaborate closely with product managers, designers, and other engineering teams to shape requirements and deliver well-scoped, high-impact capabilities.
Strengthen engineering quality: Promote best practices around performance, maintainability, and resilience across the AI platform, while mentoring and learning alongside the team.