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Salary: USD 160,000 - 220,000 / annual
Seso is modernizing the back-office for American agriculture, building the premier platform for agribusiness to hire, manage, and pay their workforce. The company addresses farmers' critical pain point—access to qualified labor—through a software-enabled solution that automates the H-2A visa process and provides a comprehensive HR platform spanning recruitment, onboarding, payroll, and insurance, while offering agricultural workers access to financial services.
The company has raised over $60M from Tier I investors including Index, Founders Fund, and Mary Meeker at Bond, and has been recognized as a Forbes Rising Star and part of Andreessen Horowitz's American Dynamism 50.
You'll join as a Software Engineer focused on AI/Agents, working on production AI agents that are core to the product experience—not prototypes or demos. This is a genuine AI-native role for someone who thinks in agents, evals, and production ML systems as a default way of building software. Seso has already shipped its first agents and is looking for a thought partner to level up the game.
Key responsibilities include designing and shipping production AI agents, working across the full stack (TypeScript, Node.js, React) to build features end-to-end, bringing ML/AI judgment to product decisions about where agents make sense and how to ensure reliability in production, and moving fast in a startup environment where you'll own real ambiguity and be empowered to push boundaries.
Must-haves: hands-on ML background and/or real experience building and shipping production AI agents (non-trivial agent design, evals, and reliability work at scale—not just API calls); strong TypeScript experience; 2+ years of professional software development experience.
Nice-to-haves: LangGraph/LangChain or similar agent framework experience; Node.js/React stack experience; forward-deployed engineering (FDE) experience and comfort being customer-facing; 3–5+ years of professional development experience; based in San Francisco (preferred).