Software Engineering Intern, Products Palo Alto

Spend a summer building production software on the platform enterprises use to run agentic apps.

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.

This is a real engineering internship. You will own a piece of work that ships, that other people depend on, and that is still running after you leave. The scope is narrower than a full-time engineer’s and you will have a direct partner accountable for your project succeeding, but the bar is the same as for anyone else here: correct, tested, and able to hold up when someone depends on it.

This is one application for engineering internships across our teams. We will match you to a team and a project after we have talked.

Core AI builds the shared runtime and intelligence systems every agent depends on: agent loops, tool execution, memory, durable workflows, model routing, and the evaluation systems that tell us whether a change actually improved anything. The feedback is fast, because an evaluation either catches a regression or it does not.

AI Services builds the governed control plane around our agents: the registry of which agents exist, the identity and delegation machinery that decides whose authority an agent acts under, the connectors that reach customer systems, and the model gateway every inference call crosses. The correctness bar is unusually visible: controls either hold or they do not.

Products builds what enterprise users see and touch:
Sycamore Forge, where customers describe what they need and watch agents build it, and Sycamore Guard, where a customer’s own AI program is registered, assessed, and approved. The interface problems are genuinely unsolved, because streaming, long-running, non-deterministic systems break most of the patterns you will have learned.

Applied AI builds directly with enterprise customers, turning a real operational workflow into an application their team uses. The platform gives you a great deal to build on, so an intern can get to something consequential quickly.

• Own a real project end to end, from scoping through implementation, testing, review, and deployment.
• Write production code that meets the same bar as everyone else’s, and go through the same review.
• Ask for help early when you are stuck, and be specific about what you have already tried.
• Document what you built well enough that the person who inherits it does not have to reconstruct your reasoning.

Depending on the team, you might work in Python cloud services on FastAPI with typed data models and PostgreSQL;
React and Type Script product surfaces with Tan Stack and Tailwind; asynchronous and streaming systems; durable workflows; protocol-based tool execution over MCP; relational and vector data; and cloud-native deployment on Kubernetes.

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.

• Currently pursuing an undergraduate or graduate degree in computer science, engineering, or a related field, or recently graduated. We care about what you have built, not your year or program.
• Evidence that you have built and shipped something real: a project with users, a substantial open-source contribution, a previous internship where you owned production code, or something you built because you wanted it to exist.
• Solid software fundamentals. You can design a data model, write an API, reason about failure, and debug code you did not write.
• The instinct to test your own work and to try the realistic path rather than the happy path.
• AI-native. You use coding agents and modern models as a force multiplier while still understanding the code you ship and being able to explain your decisions.
• Clear communication, including the ability to say when you are stuck.
• Comfort with ambiguity. The problem you are given will not arrive fully specified.

You do not need prior experience with agentic apps,…