Principal Engineer, AI Security

Your Impact at LILA

Lila Sciences is building autonomous systems for scientific discovery, and the AI infrastructure behind that work needs security built in from the start. This role sits on the IT & Security team and owns security engineering for Lila's AI and ML systems, from training and inference pipelines to model registries, MCP servers, and agent runtimes.

As the senior individual contributor for AI security, you will translate the AI security roadmap into delivered controls, drive vulnerability remediation to closure, and reduce risk across the full AI lifecycle: data, models, pipelines, and agentic systems. The work is hands on, with technical depth across cloud security, supply chain security, and AI-specific threat models.

The role is deeply cross-functional. You will partner with AI Platform, AI Research, ML Operations, Science, and AI Safety so security shows up at design time rather than after the fact. Success looks like AI infrastructure that stays continuously compliant, measurable reduction in open risk, and engineering teams that treat security as part of how they build.

What You'll Be Building

• Turn the AI security roadmap into delivered controls with measurable outcomes
• Design and implement security controls across training and inference pipelines, model registries, MCP servers, and agent runtimes
• Keep AI infrastructure continuously compliant against internal standards and applicable frameworks
• Drive vulnerability remediation across AI infrastructure, models, and dependencies to closure with system owners
• Reduce risk across data, models, pipelines, and agentic systems through threat modeling, control design, and adversarial assessment
• Partner with AI Platform, AI Research, ML Operations, Science, and AI Safety so security is embedded at design time

What You'll Need to Succeed

• Strong background in security engineering, cloud security, or DevSecOps, with hands-on experience securing production infrastructure
• Working knowledge of AI/ML systems and the ML lifecycle, plus AI-specific risks such as prompt injection, model and data poisoning, insecure agent tooling, and inference-time threats
• Experience with software supply chain security and dependency and vulnerability management
• Experience implementing data security controls (classification, data flow mapping, access control, encryption) in cloud environments
• Proven ability to partner across engineering teams and drive cross-functional remediation to closure
• Comfort operating with ambiguity in a fast-moving research environment

Bonus Points For

• Hands-on AWS security experience
• Experience with SBOM tooling at scale
• Familiarity with AI governance and assurance frameworks such as NIST AI RMF
• Experience with MAESTRO-style threat modeling for agentic systems