As this is a new initiative within a growing lab, you will be expected to actively contribute as an individual researcher and developer—leading by example through hands-on prototyping, coding, and research, while laying the technical and operational foundations for future growth.

You will work closely with internal product, engineering, and security leaders—as well as external academic, industry, and innovation partners—to position the company as a global leader in Hybrid AI Security.

Job Responsibilities:

• Serve as the technical and strategic lead of the Hybrid AI Security team.
• Define and execute the research and development roadmap for securing hybrid AI systems, LLMs, and multi-agent frameworks.
• Act as a founding individual contributor: conducting original research, building prototypes, and writing production-grade code in the early stages.
• Bridge AI-native threats with traditional security domains (e.g., application security, network security, IoT, cloud, etc.) to deliver holistic solutions.
• Guide security architecture and threat modeling for LLM deployments, agent interactions, orchestration platforms, and inference pipelines.
• Collaborate with global product and engineering teams to translate research into secure, scalable features.
• Build academic, industry, and partner relationships to expand the lab’s innovation and impact footprint.
• Represent the team in executive strategy discussions, external forums, and cross-functional innovation efforts.

Minimum Requirements:

• MSc or PhD in Computer Science, Cybersecurity, AI/ML, or related field.
• 10+ years of experience in cybersecurity R&D, with deep knowledge across multiple domains.
• 5+ years of experience in applied AI/ML research or engineering (LLMs, agents, model deployment, etc.).
• Demonstrated hands-on coding ability in Python or similar languages—especially for AI prototypes, threat simulations, or agent frameworks.
• Experience leading multidisciplinary teams while actively contributing to technical development.
• Broad understanding of security layers: Hardware, network, data, application, infrastructure, AI model lifecycle.
• Familiarity with agentic AI frameworks, LLM pipelines, and orchestration architectures.

Preferred Requirements:

• Expertise in adversarial ML, model hardening, prompt injection mitigation, or policy enforcement in AI systems.
• Knowledge in hybrid AI environments (cloud + edge), privacy-enhancing technologies, or AI governance.
• Background in one or more of: AppSec, IoT Security, SOC engineering, cloud infrastructure, zero trust.
• Experience with open-source security or AI tools, sandboxing, or code-level mitigations in production systems.
• Strong communication and product alignment skills—able to translate vision into milestones and real-world impact.