Principal Engineer, AI Security

One Charles Park, Cambridge, MAfull timeOn Site

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senior

$252k–$374k

H1B: no

Lila Sciences Principal Engineer, AI Security at Lila Sciences owns security engineering for AI and ML systems, driving vulnerability remediation and embedding security controls across AI infrastructure. The role partners cross-functionally to ensure security is integrated at design time.

Responsibilities

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.
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

Qualification

Hands-on AWS security experienceExperience with SBOM tooling at scale

Required

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