Senior Systems Engineer, Enterprise AI Platforms

CoreWeave•Sunnyvale, CA

2d•$182,000 - $242,000•Onsite

About The Position

CoreWeave is seeking a Senior AI Platform Engineer to lead the design, implementation, and governance of AI tools within the company's employee environment. This role involves being the technical owner of AI tools used daily by employees, including chat assistants, desktop apps, coding agents, AI terminals, AI browsers, and workplace knowledge tools. A key aspect of this position is ensuring the safe adoption of these tools by building security guardrails directly into the platform through identity, device, and tenant-level controls, rather than relying solely on policy documents. The ideal candidate is deeply passionate about AI, stays at the forefront of the field, and thrives in a rapidly evolving environment where tools and best practices change monthly. This is a unique, composite role blending production software and SRE engineering, hands-on LLM and agent work, and enterprise IT controls across identity, devices, and SaaS. Candidates are expected to have deep expertise in two of these three areas and a willingness to grow in the third. Success in the first year will involve providing employees with cutting-edge AI capabilities, ensuring rapid, governed rollouts of new vendor tools and features, and establishing a foundational framework for AI platform lifecycle management and governance.

Requirements

6+ years in software engineering, site reliability engineering, or IT systems engineering where you built and ran things in code, not just configured them.
2+ years of hands-on experience building with or administering LLM-based products: API integration, prompt and tool design, retrieval pipelines, agent frameworks, multi-agent workflows and agent loops, or enterprise administration of tools such as Claude, ChatGPT Enterprise, Copilot, Gemini, Glean, or Rovo. You have shipped or run something with these, not just used them.
Strong software engineering skills in Python, Go, Rust, or TypeScript: you have built services and integrations that other people depended on and kept them running.
Experience operating services in production (containers, infrastructure-as-code, CI/CD, secrets management, and observability), or you have automated enterprise systems with code (Terraform for Okta or Google Workspace, CI/CD for device and SaaS configuration, API integrations a company depended on) and are ready to go deeper on containers and observability.
A working understanding of how enterprise IT control systems function, even if you have not administered them yourself: Identity and access (how SSO, SCIM, SAML/OIDC, and OAuth grant, scope, and revoke access, and what Conditional Access, Device Trust, and Zero Trust mean in practice).
Endpoint and device management: how MDM and device-trust tooling enforce policy on a managed laptop or browser.
SaaS administration: how permission and data-sharing models in tools like Google Workspace, Slack, Atlassian, and GitHub decide who can see what.
Proven ability to produce clear technical documentation and lead complex, cross-functional technical initiatives.

Nice To Haves

Real-world experience administering one or more of these at enterprise scale: identity (Okta or Entra ID), endpoint management (Apple MDM/DDM, Jamf, Kandji, Munki, Intune, Chrome Enterprise), or SaaS platforms (Google Workspace, Slack, Atlassian, GitHub).
Experience deploying and governing enterprise AI tools across a 1,000+ person organization.
Hands-on experience building custom Model Context Protocol (MCP) servers and integrations, authoring reusable agent skills, and shaping agent behavior through system prompts and repository-level instruction files (AGENTS.md, CLAUDE.md, or equivalent).
Working knowledge of data governance for AI: data classification, retention, training opt-out, DLP, and the audit and logging capabilities of the major AI vendors.
Experience self-hosting and serving models.
Familiarity with compliance frameworks such as SOC 2, ISO 27001, SOX, and GDPR, and with emerging AI governance standards such as NIST AI RMF and ISO/IEC 42001.

Responsibilities

Own the end-to-end lifecycle of employee-facing AI platforms across every surface they ship on (web, desktop, mobile, IDE, terminal, and browser): evaluation, procurement support, tenant design, rollout, configuration management, and ongoing operations.
Keep the Platforms in Scope list current as the market moves.
Establish CoreWeave's AI operating framework for IT: intake and review for new AI tools and agents, standards for internal bots, runbooks, a clear path from pilot to supported service, and enablement content that helps employees use AI tools well and safely.
Design and implement AI guardrails as enforceable controls, in partnership with Enterprise Security and IT Identity and Access: SSO and SCIM provisioning, role- and group-based feature entitlement, data-retention and training opt-out settings, browser-extension and desktop-app policy, and device-trust requirements for access.
Build and operate connector and MCP allowlists with data-source scoping across Okta, Google Workspace, Slack, Atlassian, GitHub, ITSM, and HRIS.
Partner with Enterprise Security, Legal, and Privacy to translate acceptable-use, data-classification, and regulatory requirements into technical policy, and support audits with the evidence those controls produce.
Manage the IT-owned control layer for CoreWeave's internal chatbots and agents: the gateway, identity, secrets, MCP allowlisting, logging, rate limiting, and cost attribution in front of the serving stack, deployed on CoreWeave infrastructure alongside the teams that run it.
Manage inference endpoints, agent hosts, and developer AI tooling on managed devices, treated as managed endpoints with a lifecycle, configuration baselines, and observability.
Manage the IT-owned share of CoreWeave's internal MCP servers, reusable agent skills, and shared agent configuration (system prompts, AGENTS.md and CLAUDE.md instruction files, tool policies), plus the identity, logging, and allowlisting standards, with reference implementations, that agents and MCP servers built by other teams must meet.
Hold the AI services you own to SRE standards: automation and infrastructure-as-code for everything repeatable, reliability targets, on-call, and incident response.
Instrument usage, cost, quality, and risk telemetry across AI platforms, and build the dashboards and alerting that show leadership what is being used, by whom, and at what spend.
Serve as the senior escalation point for AI tooling issues.
Communicate clearly and proactively with executives, cross-functional partners, and employees.