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Principal Engineer, AI Security

Lila Sciences - Cambridge, MA USA

Posted Apr 16, 2026

Benefits

Parental leave
Not verified
Non-birth-parent leave
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Family-building benefits
  • Fertility benefits: Not verified
  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
Offered From the posting source checked Jun 20, 2026
Verification
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Salary
$171K-$231K From the posting source checked Jun 20, 2026
401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$116,543 U.S. median for this role
Projected growth (BLS Employment Projections)
+9.8% - Much faster than average

72% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Engineering From the posting source checked Jun 20, 2026
Seniority
Principal From the posting source checked Jun 20, 2026

Schedule

Shift type
Not verified
Weekend work
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Company

Equity
Offered From the posting source checked Jun 20, 2026

Application

Cover letter
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Assessment
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Deadline
Not stated

Where they hire

State eligibility is not yet verified.

About this role

Principal Engineer, AI Security Cambridge, MA USA 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 Usage - Develop and enforce controls to prevent sensitive data leakage through AI systems, including input/output filtering, data classification, tokenization, and secure handling of prompts, embeddings, and outputs.

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