Staff Software Engineer, AI Excellence

Prophet Security•Menlo Park, CA

About The Position

At Prophet Security, we're building an AI-native security operations platform that accelerates detection, investigation, and response. Our founding team brings over 30 years of cybersecurity experience from Abnormal Security, Expel, Mandiant, McAfee, and more. We're focused on disrupting the $400B security labor market by building the future of automated security operations. This is one of the highest-leverage roles in engineering, and it has no analog on a traditional org chart. You'll own the strategy and infrastructure for AI-native development across Prophet — architecting our codebases for AI-forward development, evaluating and integrating AI tooling, and driving adoption so PMs and non-engineering teammates can ship production code with minimal-to-no engineering involvement. This is not a machine learning role. You will not be training models. You'll be making AI agents and AI-assisted workflows first-class participants in our software development lifecycle, and you'll be the person who builds and scales a model where agents build, and humans steer. This is not a role where you execute a defined roadmap handed down from leadership. You'll identify what needs to be built, make the case for it, drive adoption across skeptics and enthusiasts alike, and own the outcome. If you're energized by that level of ownership, this is your role.

Requirements

6+ years of software engineering experience, with meaningful time spent on platform, infrastructure, or developer experience work. You've built systems that other engineers (or teams) depend on daily.
Strong software fundamentals. You can design systems, write and review production code, debug distributed systems, and reason about performance, security, and reliability.
Demonstrated experience architecting for extensibility and maintainability — designing APIs, module boundaries, and codebases that are easy for others (human or AI) to work in.
Track record of driving cross-team technical initiatives. You've proposed, championed, and delivered projects that required buy-in and coordination across multiple teams. You can point to a specific initiative you drove from proposal through adoption.
Experience architecting for AI/ML workloads or LLM-based systems.
Familiarity with AI agent frameworks and orchestration (e.g., LangChain, Claude Agent SDK, OpenAI function calling patterns).
Hands-on experience with AI coding tools (e.g., Cursor, Copilot, Claude Code, Codex, Devin, or similar). You have an informed opinion on what works, what doesn't, and where the field is heading.
Proven ability to drive alignment across engineering teams — you can make the case for AI-enablement investments to skeptical stakeholders and bring people along.
Excellent written communication; you're comfortable writing architecture docs, RFCs, playbooks, and async proposals.

Nice To Haves

Background in security
Prior experience in high growth startups

Responsibilities

Architect for AI-forward development. Design and maintain codebase architecture that makes AI agents effective contributors — well-structured root-level documentation, clear module boundaries, machine-readable configuration, and sensible defaults for data privacy and legal compliance baked in, so that AI-generated code is safe by default, not by review.
Build the AI-native development platform. Own the end-to-end infrastructure that enables the "spec → build → test → ship" loop: CI/CD pipelines that accommodate AI-generated PRs, automated validation and testing harnesses, self-healing capabilities for bad changes, and human review gates where appropriate.
Productionize AI-forward development. Turn AI-generated prototypes into deployable, observable, supportable products. Partner directly with PMs to compress the gap between a working demo and a shipped feature
Evaluate tooling and make build vs. buy calls. Stay current on AI development tools (coding agents, code review bots, testing frameworks, deployment automation). Run structured evaluations and pilots. Make clear recommendations with explicit reasoning about cost, capability, integration fit, and vendor risk.
Drive adoption across engineering. This is not "build it and they will come." Produce playbooks, run enablement sessions, define validation criteria, and work directly with product teams to integrate AI workflows into their daily development. Track and report on adoption metrics.
Define and maintain governance. Establish the "human review gate" — what categories of change require human sign-off, what can flow through autonomously. Own the policies around AI-generated code: testing requirements, security review thresholds, data access guardrails, and auditability standards.
Partner with engineering leadership. Work directly with the VPE, CTO, and other Architects to translate company priorities into an AI-enablement roadmap. Unblock product teams by solving the problems they don't have the context to solve themselves.
Mentor and raise the bar. Serve as the technical anchor for AI-native development practice at Prophet. Support growth of senior engineers through design review, pairing, and teaching them how to be effective with these tools.