Sprig is a product experience platform built for researchers who need fast, relevant user insights. Powered by AI, Sprig helps you do more research in less time by capturing and analyzing real-time feedback and behavioral data at scale. With in-product surveys, feedback buttons, session replays, and heatmaps, researchers can quickly identify user needs, refine experiences, and drive data-backed product decisions faster than ever before.
Trusted by leading companies such as Dropbox, PayPal, Robinhood, and Notion, and backed by Andreessen Horowitz, Accel, First Round Capital, and Figma Ventures.
Sprig is a product experience platform built for researchers who need fast, relevant user insights. Powered by AI, Sprig helps you do more research in less time by capturing and analyzing real-time feedback and behavioral data at scale. With in-product surveys, feedback buttons, session replays, and heatmaps, researchers can quickly identify user needs, refine experiences, and drive data-backed product decisions faster than ever before.
Trusted by leading companies such as Dropbox, PayPal, Robinhood, and Notion, and backed by Andreessen Horowitz, Accel, First Round Capital, and Figma Ventures.
What they do: AI-powered product research and product-experience platform (in-product surveys, session replays, heatmaps, feedback).
Founder / CEO: Ryan Glasgow
Founded / Rebrand: 2019; launched as UserLeap and rebranded to Sprig in August 2021
Funding: $30M announced Aug 2022; Dealroom reports $88M total raised
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Company Overview
Problem Domain
Product and UX research — rapid collection and analysis of user feedback and behavioral data to improve product experiences.
Founded
2019
Industry
Product experience / UX research SaaS
Tech Stack
AI / ML
In-product surveys
Session replays
Heatmaps
Feedback widgets
Funding Track Record
- 2022-08-02
30000000
Round announced Aug 2, 2022 with participation from Andreessen Horowitz, Accel, First Round Capital, Elad Gil, and Figma Ventures.
Investor Signal
“Backed by Andreessen Horowitz, Accel, First Round Capital; notable individual backers include Elad Gil and Dylan Field; Figma Ventures participated in the Aug 2022 round.”
Founders
What we do
Join the Team
Senior Software Engineer
On-SiteSan Francisco Bay Area, US
On-Site • San Francisco Bay Area, US
Who you are
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
Prompt and context design knowledge: Familiarity with prompt construction and context management is a strong plus
Pragmatic execution style: You balance technical depth with speed, adapt quickly, and enjoy iterating in a fast-moving AI environment
What the job involves
Benefits
Competitive Salary
Competitive Employee Equity
401K Program
Medical, Dental, and Vision Benefits
Additional Wellbeing Benefits
Generous Paid Time Off
Paid Parental Leave
Hardware & Software
Teeming tracks opportunities at over 24,000 AI startups, then works with you to find (and land) the one you'll love.
AI Researcher
InternshipCambridge, GB
Internship • Cambridge, GB
AI Researcher
Full-timeNiš, RS
Full-time • Niš, RS
DevOps Engineer
Full-timeMunich, DE
Full-time • Munich, DE
Frontend Developer
Full-timeUtrecht, NL
Full-time • Utrecht, NL
Product Designer
Full-timeNovi Sad, RS
Full-time • Novi Sad, RS
Technical Writer
Part-timeNiš, RS
Part-time • Niš, RS
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
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
