Senior 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."

• 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

• 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

• 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