About Sycamore

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 role

You will build the shared runtime and learning systems that Sycamore's product engineers and customer applications use to create dependable agents.

One half of the work is the agent runtime: multi-turn sessions, model routing, tool execution, connectors, memory, human approvals, durable workflows, and the APIs and libraries that expose those capabilities.

The other half is the intelligence loop around that runtime: collecting useful signals from production behavior, connecting feedback to business outcomes, reproducing failures offline, evaluating changes, and turning what we learn into safer and more capable versions of agents. That can include improvements to prompts, tools, skills, context, memory, retrieval, routing, policies, and—where the evidence supports it—learning or optimization techniques.

You will also carry these systems all the way into product surfaces. You will build React interfaces for composing and running agents, inspecting live traces and tool calls, reviewing evaluations, resolving approvals, and understanding why a system behaved the way it did. Core AI engineers are expected to own coherent end-to-end slices across backend services, APIs, state, and user experience—not stop at a service boundary.

You will work closely with Product engineers when a customer workflow exposes a missing primitive or a difficult failure mode. You will find the durable abstraction, build it without overfitting to one deployment, and operate it in production.

Product owns the customer-specific application and last mile. Infrastructure owns the execution and deployment substrate. Core AI owns the horizontal runtime, intelligence, and improvement capabilities that both depend on, along with the experiences that make those capabilities usable.

What you will do

Build provider-independent agent loops, state management, streaming, context handling, recovery, and graceful degradation.

Develop the learning data plane around agents: structured trajectories, feedback and outcome signals, offline datasets, lineage, privacy controls, and reliable links between an agent version and its behavior.

Create evaluation systems for task completion, tool use, long-horizon behavior, safety, latency, cost, accessibility, and business outcomes.

Build offline analysis and improvement loops that can surface failure clusters, grow golden datasets, and propose changes to prompts, tools, skills, context, retrieval, memory, policies, and model routing.

Design experiment and versioning systems for comparing changes through replay, shadow traffic, canaries, or controlled rollouts, with clear promotion and rollback criteria.

Explore where approaches such as preference learning, reward modeling, reinforcement learning, or continual learning can improve agent behavior—and recognize when a simpler evaluation or systems change is the better answer.

Build detectors that identify regressions and opportunities across quality, reliability, cost, latency, and user adoption without mistaking noisy proxy metrics for real progress.

Design typed tool interfaces and protocol-based execution across internal capabilities and customer-authorized services.

Build connectors and credential flows with explicit read/write modes, permissions, auditability, and predictable failure behavior.

Develop memory extraction and retrieval while enforcing tenant, user, and project visibility boundaries.

Build durable orchestration for long-running tasks, checkpoints, approvals, handoffs, timers, and human-in-the-loop interactions.

Publish reliable APIs, event-driven interfaces, reusable libraries, and pluggable improvement strategies that work across different agent categories and enterprise deployments.