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Sycamore is building the trusted agent operating system for the enterprise. We are a small, engineering-led team working directly with Fortune 500 enterprises, delivering an end-to-end product that maps workflows to agent-driven outcomes from UI to data models and deployment.
You will own the full loop from discovery to production debugging, turning customer-specific needs into reusable platform capabilities and collaborating with Core AI and Infrastructure to scale.
Build with enterprise customers, own the path to production, and turn repeated lessons into a better product.
Sycamore is building the trusted agent operating system for the enterprise. Our platform helps companies build, deploy, and orchestrate agentic apps that take on real operational work, with the security and control large organizations need.
We are a small, engineering-led team working directly with Fortune 500 enterprises. We have raised $65M from Coatue and Lightspeed, along with other investors and industry leaders.
The simplest way to describe this role: you are the engineer in the customer room and the product engineer who ships what the room needs. You will map how an important workflow works today, find where an agent can create a measurable outcome, and build the application end to end, from the user experience and data model through agent tools, integrations, permissions, approval points, and the operational controls needed to run safely.
You own the full loop: discovery, implementation, realistic testing, deployment, production debugging, adoption, and iteration. Customer-specific product work belongs with you, and when the same runtime, connector, memory, or deployment need appears repeatedly, you work with Core AI or Infrastructure to turn it into a reusable capability rather than maintaining parallel custom implementations.
Our current product environment includes TypeScript, React, TanStack, Tailwind, Python, FastAPI, typed data models, PostgreSQL, durable workflows, MCP-based tools and connectors, browser automation, and cloud-native deployment on Kubernetes.
We use automated tests, coding agents, traces, evaluations, and production feedback as part of everyday engineering.
This is context, not a checklist. We do not require previous experience with every language, framework, or vendor in our stack. Strong engineers who understand systems, learn quickly, and have shipped production software can become effective here without matching our tools one for one.
Former founders, early product engineers, and forward-deployed engineers often do well here when they have retained real code and production ownership. That background is a signal, not a requirement.