Software Engineer, Agent Platform Palo Alto

Sycamore Labs, Inc.; Palo Alto (CA); Hybrid; USD 180.000 - 260.000; Vollzeit

Benefits dieser Stelle

Competitive cash compensation

Meaningful equity

Zusammenfassung

Sycamore Labs, Inc. is hiring for a backend-focused engineer to build the control plane that defines agent behavior, identity, and cost. You will design authorization and credential flows, and own the model gateway, ensuring scalable routing and cost attribution across providers.

The role emphasizes security, reliability, and cross-team collaboration within a small, engineering-led team serving Fortune 500 enterprises. Competitive equity and cash compensation are offered.

Qualifikationen

• 5-12 years of software engineering experience.
• Experience building integration or API platforms with third-party systems and credentials.
• Strong backend and distributed systems fundamentals, including typed API design and production debugging.
• Security-minded approach to identity, authorization, delegation, credentials, multi-tenancy, and auditability.
• Ability to reason about retries, timeouts, and safe rollout across services.

Aufgaben

• Build and operate control-plane services registering agents, issuing and verifying identities, and enforcing permissions.
• Own the integration surface: connectors, tool execution, protocol gateways, consent flows, and read/write policy for each.
• Design authorization, delegation, and credential flows to keep customer secrets safe.
• Build and operate the model gateway: routing, provider fallback, timeouts, rate limiting, budgets, and cost attribution.
• Publish reliable, versioned APIs and contracts used by Core AI, Product, and customer apps.

Kenntnisse

Backend engineering

Distributed systems

API design

Security and IAM

Cloud & Kubernetes

Cross-functional collaboration

Ausbildung

Bachelor's degree in CS or related field

Tools

Go

Python

Jobbeschreibung

Build the control plane that decides which agents exist, what they may do, whose authority they act under, and what they cost.

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.

Where you could focus

If Core AI decides what happens inside a single agent run, AI Services decides which agents exist, what each is permitted to do, whose authority it acts under, what it costs, and how it reaches the outside world. These are chokepoints: a control that exists here holds for every agent on the platform, and a mistake here is a platform-wide mistake. The work splits into two halves, and most people end up touching both.

The agent-facing control plane. The registry that makes an agent discoverable and describable, the identity and delegation machinery that lets it act on a person’s behalf, and the integration surface through which it reaches customer systems. The central design problem is authority: an agent needs to do real work in a customer’s environment without ever holding a credential it could leak, without exceeding what the person who asked for it could do themselves, and in a way that stays attributable afterwards. A permissive default on the integration surface is not a bug report, it is an incident.

The model gateway. Every model call on the platform goes through one hop we control. That hop decides which upstream serves the request, what happens when the upstream is overloaded or rate-limited, how long the caller waits, what the call costs, whose budget it lands on, and what may be recorded about it. Concentrating that in one place is what makes the guarantees possible and what makes the work demanding: the gateway must be fast, must not become a single point of failure, and must be right about cost, because an attribution bug is not a reporting inconvenience when budgets are enforced against it. We do not run our own inference. There are no GPUs and no self-hosted serving stack; the sophistication is in routing, economics, provider abstraction, and control.

What you will do

• Build and operate the control-plane services that register agents, issue and verify their identity, and decide what they are permitted to do.
• Own the integration surface: connectors, tool execution, protocol gateways, consent flows, and the read and write policy on each.
• Design authorization, delegation, and credential flows so that agents can act without ever holding a customer secret they could leak.
• Build and operate the model gateway: routing, provider fallback, timeouts and cancellation, rate limiting, budgets, and cost attribution.
• Publish reliable, versioned APIs and contracts that Core AI, Product, and customer applications depend on.

The environment you will work in

Our current AI Services environment includes Python cloud services built on FastAPI and Pydantic; Go services for identity and token issuance; PostgreSQL; a self-hosted model gateway fronting multiple providers; protocol-based tool execution over MCP, including both outbound tool use and an inbound gateway with OAuth; OAuth credential vaulting for customer system integrations; durable workflows; and cloud-native deployment on Kubernetes.

We use automated tests, coding agents, traces, evaluations, and production feedback as part of everyday engineering, and we lean heavily on contract tests to keep independently deployed services honest with each other.

This is context, not a checklist. We do not require previous experience with every language, framework, provider, or tool in our stack. Comparable experience building API platforms, identity and authorization systems, integration platforms, gateways and proxies, or developer platforms 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.
• Experience building integration or API platforms where third-party systems, credentials, and partial failure are the daily material.
• Strong backend and distributed systems fundamentals, including typed API design, asynchronous services, persistence, reliability, and production debugging.
• A security-minded approach to identity, authorization, delegation, credentials, multi-tenancy, and auditability. Much of this role is trust boundaries.
• The ability to reason about retries, idempotency, partial failure, timeouts, cancellation, rate limiting, and safe rollout.
• Judgment about where a control belongs. You can tell the difference between a chokepoint that holds and a check that can be bypassed.
• Comfort working across languages and protocols, and reading unfamiliar code to find where a contract is actually enforced.
• AI-native. You use coding agents and modern models as a force multiplier while still owning architecture, correctness, and operational outcomes.
• Comfort with startup ambiguity, fast feedback loops, and broad ownership.

Experience with cost or usage accounting where the numbers are enforced rather than merely reported is a strong signal, and so is having operated a gateway or proxy that everything else depended on. Neither is required.

• 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.
• Build the controls that make it defensible for a large enterprise to let agents touch real systems.
• Work on identity, authorization, integration, and cost as one coherent platform rather than as scattered features.
• Own chokepoints where a single good design decision holds for every agent on the platform.
• Solve problems that are genuinely open, in a category where the right answers are still being worked out.
• Join early enough to shape the AI Services architecture.
• Receive competitive cash compensation and meaningful equity in the company you are helping build.

Hard problems, real impact

Trust architectures, memory systems, multi-agent coordination. The foundational layer that makes agentic apps work in production.

Small team, high ownership

Every engineer shapes the product and the culture. No layers of process between you and the work that matters.

Backed by the best

$65M from Coatue, Lightspeed, Abstract Ventures, Dell Technologies Capital, 8VC, and notable industry angels.

Grow with us

Competitive compensation, meaningful equity, and a genuine focus on your growth as the company scales.