Senior Security Engineer - AI Security & Platforms

Ridgeline•Reno, NV

10h•$164,000 - $205,000•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

4+ years of experience in application security, cloud security, or security-focused software engineering, including owning projects independently and partnering across teams
Demonstrated depth in at least one of: secure code review and product security; cloud and infrastructure security (AWS, IAM, guardrails); or building production software with a strong security focus
Proficiency in at least one high-level language (Python preferred; Kotlin or TypeScript a plus)
Hands-on, daily use of AI/LLM tooling, and the judgment to validate its 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
A habit of driving findings to remediation rather than just reporting them, with honest risk calibration
Strong written and verbal communication, with the ability to explain security tradeoffs clearly to engineers and product partners

Nice To Haves

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

Responsibilities

Design and build the platforms, guardrails, and reusable frameworks that enable secure AI adoption in your area, owning automation projects end to end - from design through Infrastructure-as-Code deployment and operation
Independently assess the security impact of AI-powered features and lead security reviews in the areas you support, identifying complex AI risks that generic tooling misses - prompt injection, input/output sanitization gaps, data leakage, and trust-boundary violations
Design and implement least-privilege patterns and clear trust boundaries for agentic / tool-use systems and MCP integrations, rather than only applying existing ones
Own and extend guardrails for internal AI developer tooling (e.g., Claude Code, Cursor), improving coverage as new risks and usage patterns emerge
Threat model AI features and integrations in your area to define security requirements before they are built, partnering with engineering so the secure path is also the easy one
Own triage and remediation tracking for AI-related findings in your area, driving SLA compliance, validating fixes, and using AI to accelerate root-cause analysis and variant detection across related findings
Design and build AI-augmented security tooling and pipelines that multiply review capacity, writing production-quality code and reviewing peers' code and Infrastructure-as-Code for security correctness, while keeping false positives low enough that engineering trusts the results
Act as the primary AI Security partner for multiple engineering teams, sought out for design input on new AI features rather than only post-hoc review
Develop recognized expertise in one or more AI security areas (prompt-injection defense, agent/MCP security, internal AI tooling guardrails) and own coverage for that area
Use AI fluently every day to accelerate and scale your work, validating its output for correctness and risk