Staff Security Engineer - AI Security & Platforms

Location: San Ramon, CA; Reno, NV

Ridgeline is the industry cloud platform for investment management. It was founded by visionary tech entrepreneur Dave Duffield (co-founder of both PeopleSoft and Workday) to apply his successful formula of solving operational business challenges with bold innovation and human connectivity to the unique needs of the investment management industry.

Ridgeline started with a clean sheet of paper and a deep bench of experts bound by a set of core values and motivated to revolutionize an industry underserved by its current tech offerings. We are building a new, modern platform in the public cloud, purpose-built for the investment management industry and we are prioritizing security, agility, and usability to empower business like never before.

With a growing campus in Reno and offices in New York, Lake Tahoe, and the Bay Area, Ridgeline is proud to have built a fast-growing, people-first company that has been recognized by Fast Company as a "Best Workplace for Innovators," by The Software Report as a "Top 100 Software Company," and by Forbes as one of "America's Best Startup Employers."

• 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.

• 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.

• 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).