Principal Engineer, AI Security
About this role
Engineers who thrive in ambiguous research environments and enjoy tackling complex security challenges across the full machine learning lifecycle will find a natural home at Lila Sciences. Operating as the senior individual contributor for AI security within the IT and Security group, this practitioner takes charge of protecting autonomous systems designed for scientific discovery. Daily responsibilities include translating strategic security roadmaps into concrete technical controls, mitigating infrastructure vulnerabilities, and embedding safety directly into model registries, training pipelines, and agent runtimes.
Collaboration forms a cornerstone of the position, requiring close alignment with AI Platform, Research, ML Operations, Science, and Safety teams to ensure threat mitigation happens at design time rather than post-deployment. Success is measured by maintaining continuous compliance, reducing measurable risk across data and agentic architectures, and fostering a culture where engineering groups view security as an inherent part of development. Candidates who bring deep technical expertise in cloud infrastructure and AI threat models will significantly shape the secure foundations of next-generation autonomous science.
What you will do
- Translate the overarching AI security roadmap into delivered controls with measurable outcomes.
- Design and implement security measures across training pipelines, inference paths, model registries, MCP servers, and agent runtimes.
- Maintain continuous compliance of AI infrastructure against internal standards and applicable regulatory frameworks.
- Drive vulnerability remediation across AI infrastructure, models, and dependencies to full closure alongside system owners.
- Reduce risk across data, models, pipelines, and agentic systems through threat modeling, control design, and adversarial assessment.
- Partner closely with AI Platform, AI Research, ML Operations, Science, and AI Safety teams to embed security at design time.
What you need
- Bring a strong professional background in security engineering, cloud security, or DevSecOps with hands-on experience securing production infrastructure.
- Possess working knowledge of AI and ML systems, the machine learning lifecycle, and specific threats such as prompt injection and data poisoning.
- Demonstrate experience with software supply chain security, dependency tracking, and vulnerability management.
- Show documented experience implementing cloud data security controls including classification, data flow mapping, access control, and encryption.
- Exhibit proven ability to partner across diverse engineering teams and drive complex cross-functional remediation efforts to completion.
- Maintain comfort operating with high ambiguity within a fast-moving scientific research setting.
Nice to have
- 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
Benefits
- Comprehensive medical, dental, and vision coverage
- Employer-paid life and disability insurance
- Flexible time off with generous company-wide holidays and paid parental leave
- Educational assistance program and commuter benefits including bike share memberships
- Company subsidized lunch program
- Competitive base compensation with bonus potential and generous early-stage equity
Skills and tech stack
Questions candidates ask
What is the compensation range for the role?
The expected base salary range is $252,000 to $374,000 USD. The position also includes bonus potential, generous early-stage equity, and a comprehensive benefits program.
Where is the position located?
The role is located in Cambridge, Massachusetts, USA. USD salary ranges apply specifically to U.S.-based positions.
What team will I be working with?
This role sits on the IT & Security team as the senior individual contributor for AI security. You will partner extensively with AI Platform, AI Research, ML Operations, Science, and AI Safety.
What does success look like in this position?
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.