Principal Engineer, AI Security
Technologies
LLM APIs · AI gateways · AWS · GCP · zero trust · DLP · data classification · prompt injection · red teaming · SaaS security
About Lila Sciences
Autonomous AI platform that executes the scientific method end-to-end—from hypothesis generation to experimental execution—for biotech, materials, and chemical R&D.
Series A
Job description
The full responsibilities and requirements are on the employer’s site.
Read the job description ↗Source and classification
Internal deployment & tooling · Evidence for this classification:
Your Impact at LILA As a Principal Security Engineer focused on AI Security, you will define and drive the technical strategy for securing how AI is used across Lila's enterprise. You will operate as a senior individual contributor, partnering with IT and business teams to ensure safe and compliant adoption of AI tools and platforms. While Lila builds AI-powered systems, this role is primarily focused on securing the use of third-party and internally deployed AI tools across the enterprise — ensuring sensitive data, intellectual property, and scientific workflows are protected as AI becomes deeply embedded in how work gets done. What You'll Be Building Enterprise AI Security Strategy — Define and implement security controls and guardrails for the use of AI tools (e.g., LLM APIs, SaaS AI platforms, and internal AI services) across the organization. AI Gateway & Agentic Gateway
More from the job description
Your Impact at LILA As a Principal Security Engineer focused on AI Security, you will define and drive the technical strategy for securing how AI is used across Lila's enterprise. You will operate as a senior individual contributor, partnering with IT and business teams to ensure safe and compliant adoption of AI tools and platforms. While Lila builds AI-powered systems, this role is primarily focused on securing the use of third-party and internally deployed AI tools across the enterprise — ensuring sensitive data, intellectual property, and scientific workflows are protected as AI becomes deeply embedded in how work gets done. What You'll Be Building Enterprise AI Security Strategy — Define and implement security controls and guardrails for the use of AI tools (e.g., LLM APIs, SaaS AI platforms, and internal AI services) across the organization. AI Gateway & Agentic Gateway Security — Design and implement AI gateway controls to manage and monitor access to external and internal AI systems. Secure agentic workflows by enforcing identity, authorization, tool-use constraints, and policy controls for autonomous or semi-autonomous agents. AI Red Teaming & Adversarial Testing — Conduct red teaming and adversarial testing focused on enterprise AI usage, including prompt injection, data exfiltration, jailbreaks, and abuse of connected tools and plugins. Data Protection for AI [... source excerpt omitted ...] exposure, policy violations, or misuse of AI tools. Cross-Functional Technical Leadership — Partner with Legal, Compliance, IT, and Engineering to align AI usage with regulatory requirements, data governance policies, and responsible AI practices. Security Enablement — Contribute to internal guidance and education on safe AI usage, including secure prompting, data handling, and appropriate use of AI tools. Security Tooling & Implementation — Evaluate and implement tooling for AI security, including AI gateways, DLP integrations, monitoring solutions, and policy enforcement mechanisms. What You’ll Need to Succeed 8+ years of experience in information security, with strong expert
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