Core AI Engineer

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.
• Build and refine React product surfaces for agent configuration, live execution, traces, evaluations, approvals, and improvement workflows.
• Own full-stack product slices across UI state, streaming, typed APIs, backend services, and persistence, with attention to accessibility, responsiveness, and failure recovery.
• Translate powerful platform primitives into product interactions that enterprise users can understand and trust.
• Debug difficult failures across models, prompts, tools, state, data, experiments, APIs, and distributed services.
• Join customer conversations when a repeated requirement or production outcome reveals a missing horizontal capability.
  
  

The environment you will work in

Our current Core AI environment includes Python cloud services; React and TypeScript product surfaces; 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.
• Practical experience shipping LLM, agent, search, retrieval, recommendation, experimentation, workflow, or developer-platform systems into production.
• 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.
• Clear communication and high EQ. You can work with Product, Infrastructure, customer engineering teams, and applied AI researchers without losing technical depth.
• A generalist mindset and comfort moving between libraries, services, data, experiments, integrations, evaluations, and product surfaces.
• Comfort building product-facing software. You can work in React and TypeScript when a capability needs a great interface, and you can reason about streaming state, accessibility, and end-to-end user experience.
• Comfort with startup ambiguity, fast feedback loops, and broad ownership.
  
  

Experience with reinforcement learning, preference learning, reward modeling, post-training, continual learning, ranking, causal inference, or large-scale experimentation is valuable but not required. We care more about whether you can connect learning ideas to production evidence and reliable systems than whether you have used a particular technique.

Engineers from agent-runtime, distributed-workflow, retrieval, recommendation, experimentation-platform, integration-platform, developer-platform, or production applied-AI backgrounds often do well here. We care more about the systems you personally built, measured, and operated than a particular company, school, language, or model vendor.

What this role is not

• It is not a prompt-engineering role detached from software and systems engineering.
• It is not pure foundation-model research. It combines empirical AI work with production systems engineering.
• It is not ungoverned online self-modification or a system that optimizes a metric without understanding its consequences.
• It is not generic infrastructure or cluster ownership.
• It is not the primary owner of one-off customer application delivery.
• It is not a narrow backend role disconnected from agent behavior, learning, and product outcomes.
  
  

Interview process

• 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.
  
  

Why join

• Build the cloud services that help enterprise agents learn from production experience.
• Work across agent runtimes, tools, connectors, memory, durable workflows, evaluations, experimentation, security, and product.
• Ship Core AI capabilities end to end, 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.