Cato Networks is the converged network and security cloud for the AI era.

As AI reshapes cybersecurity, enterprises face a dual challenge: unlocking AI’s potential while defending against AI-driven attacks. Cato addresses both through a unified, cloud-native platform, providing the visibility and context to prioritize risks and stop threats in real time across users, applications, data, and AI agents.

Join us as we scale: take on ambitious technological challenges and shape the next generation of enterprise security at a company trusted by thousands of organizations worldwide.

As part of this exciting journey, Cato recently joined forces with Aim Security — a fast-growing innovator redefining how organizations secure and build with AI. Backed by Cato’s global reach and powered by Aim’s startup energy, we’re building the future of AI Security together.

We are on a mission to enable enterprises to adopt AI safely and securely. Our approach combines advanced AI research with practical implementation to offer seamless integration and protection for our clients' AI based tools and infrastructure.

We are looking for a Senior Data Scientist / Applied AI Researcher with deep expertise in NLP and LLM-based systems, and the ability to operate across the full lifecycle, from problem formulation and research design to production deployment and real-world iteration.

You won’t be working on isolated experiments. You’ll be part of a multidisciplinary team that owns the system end-to-end and is responsible for how it behaves under real production traffic.

This role requires strong scientific discipline and the maturity to balance innovation with practical constraints.

What you’ll do

• End-to-End Ownership: Lead AI security solutions from problem formulation and research design through model development, evaluation, and production deployment. Ensure solutions are measurable, reliable, and effective under real-world traffic.
• Drive Technical Direction: Take responsibility for ambiguous AI security problems, propose thoughtful approaches, and drive structured experimentation and deep model analysis.
• Build for Scale: Design systems that are not only accurate, but scalable, efficient, and robust.
• Staying Updated: Continuously explore advances in NLP and LLM-based systems, and critically assess what is worth integrating into a production environment.