Member of Technical Staff, Agentic Systems

Build the cloud services that turn production agent behavior into measurable, governed improvements.

Travel: Occasional travel to customer sites

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: multi-turn sessions, model routing, tool execution, connectors, memory, human approvals, durable workflows, and the intelligence loop around them that turns production signals into safer and more capable versions of agents.

Nothing here rewrites itself unsupervised. Improvements are grounded in evidence, versioned, measured against reproducible tasks, reviewed when risk requires it, and deployable with clear rollback paths. You will carry these systems into Sycamore Forge as well, owning coherent end-to-end slices from cloud service and API design through the React experience rather than stopping at a service boundary.

What you will do

• 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, and cost.
• Design experiment and versioning systems for comparing changes through replay, shadow traffic, canaries, or controlled rollouts, with clear promotion and rollback criteria.
• Build durable orchestration for long-running tasks, checkpoints, approvals, handoffs, and human-in-the-loop interactions, with typed tool interfaces, protocol-based execution, and memory retrieval that enforces tenant, user, and project visibility boundaries.
• Publish reliable APIs, event-driven interfaces, and reusable libraries that work across agent categories and enterprise deployments.

The environment you will work in

Our current Core AI environment includes Python cloud services; React and TypeScript product surfaces in Sycamore Forge; asynchronous and streaming systems; typed APIs and data models; relational and vector data; durable workflows; protocol-based tool execution; multiple model providers; and cloud-native deployment.

We use coding agents, automated tests, traces, evaluations, browser automation, offline replay, cost and latency signals, and production feedback as part of everyday engineering. We are building toward a governed collect, learn, evaluate, and apply loop rather than a single monolithic training system.

This is context, not a checklist. We do not require previous experience with every language, framework, model provider, cloud platform, database, or infrastructure tool in our stack. Comparable experience building distributed runtimes, experimentation platforms, retrieval or recommendation systems, workflow engines, developer platforms, or production AI systems is highly relevant.

What we are looking for

• 5-12 years of software engineering experience. We will make exceptions for exceptional people in either direction.
• Strong backend and distributed-systems fundamentals, including typed API design, asynchronous workflows, persistence, reliability, and production debugging.
• An empirical approach to AI quality: you can form a hypothesis, design a useful evaluation, interpret noisy evidence, and distinguish a real improvement from movement in a proxy metric.
• The ability to reason about retries, idempotency, partial failure, long-running state, concurrency, latency, cost, experiment design, and safe rollout.
• A security-minded approach to multi-tenant systems, identity, authorization, credentials, tool execution, privacy, and auditability.
• Product judgment. You can find a durable abstraction behind a real requirement without generalizing too early or freezing customer-specific behavior into the platform.
• AI-native. You use coding agents and modern models as a force multiplier while still owning architecture, correctness, evidence, and operational outcomes.
• Comfort building product-facing software. You can work in React and TypeScript when a capability needs a great interface, and reason about streaming state and accessibility.
• Comfort with startup ambiguity, fast feedback loops, and broad ownership.

Experience with reinforcement learning, preference learning, post-training, ranking, causal inference, or large-scale experimentation is valuable but not required. We care more about the systems you personally built, measured, and operated than a particular technique, company, school, language, or model vendor.

Interview process

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

Why join

• Build the cloud services that help enterprise agents learn from production experience.
• Ship Core AI capabilities end to end in Sycamore Forge, from cloud service and API design through the React experience customers use.
• Turn production outcomes into governed improvements used across customers.
• Shape how feedback-driven learning, agent evaluation, and safe self-improvement work in a high-trust enterprise setting.
• Join early enough to shape the Core AI architecture and the India engineering team.
• Work as one engineering team across India and Palo Alto.
• Receive competitive cash compensation and meaningful equity in the company you are helping build.