Member of Technical Staff, Applied AI

About Sycamore

Sycamore is building the trusted agent operating system for the enterprise. Our platform helps companies build, deploy, and orchestrate AI agents 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.

About the role

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.

What you will do

• Turn ambiguous enterprise problems into clear technical designs, working software, and measurable outcomes.
• Build product surfaces for agent conversations, tasks, progress, approvals, artifacts, and operational workflows.
• Design APIs and relational data models that accurately represent the customer's domain and remain understandable as the product evolves.
• Build agent instructions, tool interfaces, connector interactions, and durable workflow steps with explicit permission and failure behavior.
• Navigate enterprise requirements around authentication, authorization, tenant isolation, data handling, deployment, observability, and change management.
• Test realistic user journeys rather than stopping at isolated units or happy-path demos.
• Travel to customer sites when working alongside users will accelerate discovery, delivery, or adoption.

The environment you will work in

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.

What we are looking for

• 5-12 years of software engineering experience. We will make exceptions for exceptional people in either direction.
• Strong software fundamentals across product design, APIs, data modeling, distributed systems, testing, and production debugging.
• Evidence that you personally shipped and operated meaningful software, rather than stopping at prototypes or delegating the production work.
• Product judgment. You can find the real problem behind a request, choose a useful first version, and decide what should remain customer-specific versus become reusable.
• Comfort moving across user interfaces, backend services, data, agent behavior, integrations, and deployment when the outcome requires it.
• A security-minded approach to enterprise data, permissions, credentials, approvals, and auditability.
• AI-native. You use coding agents and modern models as a force multiplier while still owning the architecture, understanding the code, and explaining important decisions.
• Clear communication and high EQ. You can work with a customer's CTO, operators, security team, and engineers without losing technical depth.
• Comfort with startup ambiguity, fast feedback loops, broad ownership, and regular customer travel. 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.

What this role is not

• It is not a pre-sales role that stops at a demo or architecture diagram.
• It is not a solutions role that hands requirements to another engineering team.
• It is not custom development without product judgment; repeated needs should improve the platform.

Interview process

• A 30-minute introductory conversation.
• Two 60-minute technical interviews, one focused on systems design and one on coding.
• A take-home assignment where you build and present a real solution using the tools you would use on the job.

Why join

• Work on AI systems that move from enterprise problems to production outcomes quickly.
• Own the complete path from workflow discovery through adoption.
• Shape both individual deployments and the product used across customers.
• Join early enough to define how the engineering team operates and grows.
• Receive competitive cash compensation and meaningful equity in the company you are helping build.