AI Security Research

At Reco.ai, we redefine AI security. Our AI-driven security platform helps organizations discover, analyze, and secure their AI applications and identities. As AI adoption grows, so do security risks — our mission is to help businesses answer one critical question: "Is my AI environment truly secure?"

We're looking for an AI Security Researcher to join us and uncover emerging AI threats, derive detection logic, discover new attack vectors, conduct red teaming, and identify insider threats and misconfigurations.

Responsibilities

What You'll Do

• Deep-dive into AI usage risks, including threat detection for chat platforms and AI agents.
• Research emerging agent threats and LLM attacks, such as jailbreaks and prompt injection.
• Conduct red teaming and large-scale experiments on AI agents, including quantitative experimentation and advanced result analysis.
• Assess internal and externally-facing AI agents for security posture and risk exposure.
• Evaluate and attack real-time defenses and runtime protections for AI applications.
• Conduct research across cloud, web, and API security to uncover novel threats and attack vectors relevant to AI systems.
• Partner with product and engineering teams to turn research findings into detection logic, guardrails, or product features.
• Stay current on and contribute to industry frameworks (e.g., OWASP LLM Top 10, MITRE ATLAS) — potentially contributing back to the community.
• Publish blog posts, give talks, and represent research at conferences.
  
  

• Background in AI/ML or security research. Ideal candidates have both, but we're equally open to candidates from data science or security research backgrounds with demonstrated depth in AI systems.
• 5+ years of experience in the AI or security industry.
• Solid understanding of AI agents: how tool use, memory, planning, and orchestration layers interact, and where that architecture introduces risk.
• Working knowledge of LLM and agentic security — prompt injection, jailbreaks, tool-use exploitation, memory/context poisoning, and related attack classes.
• Knowledge of AI vulnerabilities at both the model and infrastructure layers, and familiarity with effective mitigation strategies (e.g., guardrails, sandboxing, input/output filtering, access controls).
• In-depth understanding of LLMs, generative AI, agentic systems, and RAG pipelines — how they're built, where they break, and how failure modes propagate through a system.
• Strong research methodology and judgment: experience designing large-scale experiments, forming hypotheses, and applying quantitative rigor to evaluate results, identify signal from noise, and draw defensible conclusions from complex or ambiguous data.