> Markdown version of [/jobs/ext/4443138-senior-engineer-backend-ai-systems](https://www.wearedevelopers.com/jobs/ext/4443138-senior-engineer-backend-ai-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Engineer, Backend & AI Systems - **Company:** Varite Inc - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $135,200.0 - $146,474.0 - **Contract:** Temporary to permanent - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Microsoft Azure, Software Quality, Extract Transform Load (ETL), Data Presentation, Data Visualization, Data Intelligence, PostgreSQL, Node.Js, Raw Data, Next.js, TypeScript, Datadog, LangChain, ReactJS, Large Language Models, Prompt Engineering, Caching, Backend, Langfuse — LLM Observability and Analytics Platform, Webpack, LangSmith, Kubernetes, Graphql, Front End Software Development, Virtual Agents, JestJS, Data Pipelines, Docker - **Published:** October 31, 2026 - **Apply:** https://www.juju.com/job/16_8cf5a58e ## About the Role * 6+ years of backend engineering experience, with strong TypeScript/Node.js fundamentals * Hands-on experience building with LLMs: prompt engineering, structured outputs, tool use, retrieval, caching strategies, and cost/latency tradeoffs * Experience with agentic architectures: multi-step reasoning, tool-calling agents, orchestration patterns, and the failure modes that come with them * You think carefully about eval infrastructure, not just whether the model works today, but how you'd know if it regressed tomorrow * You write code that's clear, well-typed, and doesn't require a comment to explain what it does. You are comfortable moving quickly and leveraging AI assistance. You can understand tradeoffs, particularly when it comes to moving quickly and code quality. Bonus Points: * A genuine enthusiasm for data visualization. You find yourself opinionated about when to use a bar chart vs. a line chart, and you know why it matters * Experience with LangSmith, LangFuse, or similar LLM observability tooling * Familiarity with React and willingness to read and occasionally contribute to frontend code * Prior experience in a design-adjacent or creative tools product Education: Tech We Use * Node.js, TypeScript, GraphQL, Apollo, PostgreSQL, Drizzle ORM, Langchain, Vercel, Docker, Kubernetes, Nx monorepo, React 19, Vite, Vitest, Storybook, AWS, Azure ## Description * Client is building an AI-native data visualization creative platform, built to turn anyone into a confident data storytelling. Our app, which is in the early alpha stages, helps people go from raw data to compelling, publication-quality charts without needing to be a data scientist or designer. * We're a small, fast-moving incubator team that ships production software and cares deeply about the craft. We're looking for a senior backend engineer with a strong AI systems background to help us build the intelligence layer that makes our service feel like it actually understands data. * You'll work across our fluency engine, composition pipeline, and agentic infrastructure, and have a direct hand in how the product reasons about data and communicates with users. * We're a small team with real ownership. The problems are genuinely hard, the feedback loop is short, and the work ships to real users inside one of the largest creative platforms in the world. What You'll Work On: * Design and evolve our GraphQL API and the services behind it (Node.js, Postgres, S3, Docker/Kubernetes) * Build and improve AI-powered features: chart recommendation, data fluency, narrative generation, and agentic workflows that act on user intent * Own the eval infrastructure by defining what "good" looks like for our AI systems, build LLM-as-judge pipelines, and drive quality improvements from signal * Work across our composer and fluency engines: the systems that translate user data and intent into visual output * Contribute to architecture decisions: schema design, data loading and processing patterns, and pipeline orchestration * Partner closely with frontend engineers and designers (who often work directly in the codebase) to ship full features end-to-end