Build and operate backend systems for AI-powered customer insights, including high-throughput data pipelines and inference orchestration. Requires 5+ years backend experience, TypeScript proficiency, and applied AI integration skills.
180k – 260k/yr
Hybrid5+ YOEBackend Engineering
About the role
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.
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.
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.
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