Your Impact at LILA

Lila Sciences is building the world's first scientific super intelligence platform. As AI agents and automated systems handle more of the research and operations workload, securing them has become a core focus for the IT & Security team.

This Co-Op role focuses on the AI side of that work. You will join the IT & Security team and contribute to projects that evaluate, harden, and monitor the AI tools, agents, and automation pipelines Lila depends on. Expect exposure to areas most academic programs don't cover yet: agent governance, prompt-level threats, model deployment risks, and AI-driven security automation.

The role is structured for someone curious, technical, and looking to build applied experience. You will own specific projects, work alongside security engineers, and finish the term with hands-on work in a domain that is still being defined.

What You'll Be Building

• Assist in identifying and analyzing AI/ML security vulnerabilities, including prompt injection attacks, model poisoning, and data poisoning risks
• Participate in threat modeling exercises for AI systems and recommend security mitigations
• Review and test AI/ML application code and configurations for security issues
• Research emerging AI security threats and compile findings into technical documentation
• Develop and run security tests and proof-of-concepts for AI model robustness
• Collaborate with cross-functional teams including ML engineers, product managers, and security architects

What You'll Need to Succeed

• Currently enrolled in a Computer Science, Cybersecurity, Information Security, or related program.
• Foundational understanding of cybersecurity concepts: authentication, encryption, network basics.
• Basic familiarity with how large language models and AI agents work.
• Comfortable scripting in Python or a similar language.
• Clear written communication for documenting security findings.
• Strong attention to detail and willingness to dig into unfamiliar systems.

Bonus Points For

• Coursework or projects involving AI/ML security, adversarial ML, or LLM red-teaming.
• Exposure to cloud platforms (AWS, GCP, or Azure) and their security primitives.
• Familiarity with security tooling: SIEMs, EDR, or vulnerability scanners.
• Hands-on experience building or breaking AI agents, prompt pipelines, or RAG systems.