Staff Security Engineer - AI Security & Platforms

Ridgeline•Reno, NV

1d•$205,000 - $256,000•Onsite

About The Position

Ridgeline is building a new AI Security & Platforms team focused on secure AI enablement for the business: building the platforms that support safe AI adoption, and keeping current with how AI is used, the security issues that emerge, and how to prevent them. As a Staff AI Security Engineer, you will own this space at a technical-leadership level. You will architect secure-by-default AI platforms, lead on AI/LLM threat security, partner with teams to embed security into how they build and use AI, and enforce the organization's standards for responsible, secure AI use. Because the domain is still young, we are looking for someone who has already shown strong skills in application security, cloud security, or security-focused software development, and who pairs that with real depth in AI. At Ridgeline, the workplace culture is just as important as the products we build. We value ownership, transparency, and a bias toward action - which means we're always looking for solutions rather than just identifying problems. We're a team that chooses growth over comfort, owns our setbacks as much as our wins, and thrives on the kind of collaboration that pushes everyone to do their best work. If that's the environment where you do your best work, we would be interested to meet you. You must be work authorized in the United States without the need for employer sponsorship.

Requirements

8+ years in application security, cloud security, or security-focused software engineering, including leading complex cross-functional initiatives.
Demonstrated depth in at least one of: secure code review and product security; cloud and infrastructure security (AWS, IAM, guardrails, policy-as-code); or building production software with a strong security focus.
Advanced proficiency in at least one high-level language (Python strongly preferred; Kotlin or TypeScript a plus).
Hands-on, daily use of AI/LLM tooling, the ability to engineer systems that use it, and the judgment to validate AI output for correctness and risk. This is essential to the role.
Applied experience securing or building AI/LLM-powered systems, or a clear track record of getting deep in a new technical domain quickly.
Sound architectural judgment, a habit of driving findings to systemic remediation, and honest risk calibration.
Strong written and verbal communication, with the ability to explain security tradeoffs clearly to engineers, product partners, and leadership.

Nice To Haves

Hands-on experience with prompt-injection prevention, LLM input/output sanitization, or agent / tool-use / MCP security.
Experience building developer platforms or security guardrail frameworks that scale across an organization.
Contributions to AI security research, open source security tooling, or emerging AI security standards.
Familiarity with cloud-native security at scale (AWS) and infrastructure-as-code (e.g., Terraform).

Responsibilities

Architect and build the platforms, guardrails, and reusable frameworks that let Ridgeline adopt AI safely at scale, driving secure-by-default patterns that close entire risk classes before systems are built.
Find and mitigate the AI-specific risks that generic tooling misses: prompt injection, model input/output sanitization, data leakage, and trust-boundary violations across AI-powered features and agents.
Secure agentic and tool-use systems, including function/tool calling and MCP server integrations, with clear trust boundaries and least-privilege patterns for AI agents.
Establish and operate guardrails for internal AI developer tooling (e.g., Claude Code, Cursor, etc.) that let engineering move fast without sacrificing security.
Threat model new AI features and infrastructure to define security requirements before they are built, partnering with engineering and product teams so the secure path is also the easy one.
Build AI-augmented security tooling and pipelines that extend the team's capacity while keeping false positives low enough that engineering trusts the results.
Set organizational standards for responsible and secure AI use, risk acceptance, and model and vendor evaluation, focused on reducing how much AI risk enters the business in the first place.
Influence AI security decisions across engineering organizations, earning design input from Staff+ engineers through technical credibility rather than policy mandates.
Mentor engineers and set team standards for effective, responsible AI use, raising the capability of the whole team.
Use AI daily in your own work, build systems that use it, and validate its output for correctness and risk, setting the example others in the organization learn from.