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Sycamore

Enterprise trusted AI agent operating system

Forward Deployed Engineer

AI Application Engineering +1Updated on Nov 13
Full-time

About the Job

Requirements

  • Have 2–12 years of software engineering experience; exceptions may be made for exceptional candidates in either direction.
  • Have strong software fundamentals across product design, APIs, data modeling, distributed systems, testing, and production debugging.
  • Show evidence of personally shipping and operating meaningful software, rather than stopping at prototypes or delegating production work.
  • Exercise product judgment to identify the real problem behind a request, choose a useful first version, and distinguish customer-specific work from reusable capabilities.
  • Be comfortable working across user interfaces, backend services, data, agent behavior, integrations, and deployment as needed.
  • Take a security-minded approach to enterprise data, permissions, credentials, approvals, and auditability.
  • Use coding agents and modern models effectively while owning the architecture, understanding the code, and explaining important decisions.
  • Communicate clearly with customers’ CTOs, operators, security teams, and engineers while maintaining technical depth.
  • Be comfortable with startup ambiguity, fast feedback loops, broad ownership, and regular customer travel.

Responsibilities

  • 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 customer domains and remain understandable as the product evolves.
  • Build agent instructions, tool interfaces, connector interactions, and durable workflow steps with explicit permission and failure behavior.
  • Address 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 demonstrations.
  • Travel to customer sites when working alongside users will accelerate discovery, delivery, or adoption.
  • Own discovery, implementation, realistic testing, deployment, production debugging, adoption, and iteration.
  • Develop customer-specific product work and collaborate with Core AI or Infrastructure to turn recurring runtime, connector, memory, or deployment needs into reusable capabilities.

About the Company

Sycamore

Enterprise trusted AI agent operating system

Sycamore Labs builds a trusted agent operating system for enterprises, providing the infrastructure to safely build, deploy, manage, and observe large fleets of AI agents at scale within a secure, governed environment. Its product uses a tiered trust model: new agents begin under heavy supervision and progressively gain autonomy as they prove reliability, with human operators guiding tasks through natural language. The platform focuses on governance, security, observability, and multi-agent coordination to prevent uncoordinated digital chaos and to integrate AI agents with legacy systems. What sets Sycamore apart is its explicit emphasis on trust architectures, centralized oversight, and scalable AI governance, aiming to turn isolated demos into controllable, enterprise-wide automation. The company's goal is to unlock a wave of enterprise automation by enabling AI agents to operate safely and autonomously under policy compliance and security constraints.

Company size
11-50
Company stage
Seed
Total funding
$65M
Headquarters
Palo Alto, CA
Founded
2025

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