Senior Frontend Engineer (Conversational & General User Interface)

Join Farther, a rapidly growing venture-backed firm, as a Senior Frontend Engineer. In this role, you will work on the company's AI assistant, contributing to both the framework that enables its functionality and the assistant itself. You will design the system that determines what a screen can be and establish guardrails for unexpected inputs. Your work will have a direct impact on the user experience and the success of the company.

• Contribuer à la fois au cadre qui rend cela possible et à l'assistant lui-même, en concevant le système qui décide de ce qu'un écran peut être.
• Construire le cadre sous-jacent, afin que toute surface de Farther puisse déclarer ce que l'agent peut faire et ce qu'il peut voir en quelques lignes.
• Négocier et versionner le contrat de liaison avec l'équipe backend de l'agent, et tenir les deux côtés à cela avec des tests.

• Comfortable context-switching across multiple concurrent workstreams
• Fluent in an AI-assisted development loop — building with Claude Code, shipping through Git-based PRs and automated review
• Distributed systems fundamentals — timeouts, retries, idempotency, failure isolation
• Genuine product instincts — you care about the user experience, and raise ambiguity as a decision rather than absorbing it as a guess
• Composition over configuration; modular, scalable frontend patterns are how you think by default
• Strong knowledge-sharing / documentation habit
• 6+ years building production web applications, including time somewhere the frontend was the product
• Production experience with LLMs — agentic systems, tool calling, streaming responses, conversational interfaces — and a point of view on what an interface owes a user when the model is slow, wrong, or halfway through an answer
• Expert-level frontend engineering, where strict typing is how you already work
• End-to-end ownership and a track record of meeting deadlines
• Used or extended a framework for agent-to-UI communication, and can say precisely where it stopped being enough
• Built and versioned a component library other teams depended on, and know what a breaking change costs once they have all imported it
• AWS fluency — Lambda, API Gateway, DynamoDB, CloudWatch
• Turned LLMs on how your own team builds: codegen, large-scale migrations, spec-to-code pipelines
• Engineering platform or internal tooling — Vercel, an internal PaaS, or the developer platform at your last company
• Wealth management, financial services, or analytics products dense with numbers people act on — including the craft that keeps them fast: profiling, bundle size, render performance