We are looking for a unique talent to bridge the gap between low-level system observability and high-level AI reasoning.
You will sit at the intersection of our deep tech initiatives: actively developing our eBPF agent (Cimon) while simultaneously leading the charge on our AI innovation security research.
In this role, you will be the architect of our "security brain." You will write the low-level code that observes what is happening (eBPF/Golang) and build the AI models that understand, diagnose, and prevent issues (LLMs/Python).
You will sit at the intersection of our deep tech initiatives: actively developing our eBPF agent (Cimon) while simultaneously leading the charge on our AI innovation security research.
In this role, you will be the architect of our "security brain." You will write the low-level code that observes what is happening (eBPF/Golang) and build the AI models that understand, diagnose, and prevent issues (LLMs/Python).
Requirements:
The Core Stack:
Systems: 3+ years of experience with Golang and Linux Kernel development (eBPF or Kernel modules).
AI/ML: Hands-on experience with LLMs, prompt engineering, and Python-based data analysis.
Security: Deep understanding of SAST/SCA tools (e.g., SonarQube, Bearer, Snyk) and Container Security (Docker, K8s, Trivy).
Technical Qualifications:
Strong knowledge of Linux systems design, networking, and OS internals.
Proficiency in Python (for AI research) and Go/C (for Agent development).
Experience in analyzing container build pipelines and identifying vulnerability origins.
Ability to distill complex topics (both kernel-level and AI-level) for diverse audiences.
Bonus Points:
Experience with Rego/Open Policy Agent (OPA).
Publications or presentations at venues like KubeCon, Black Hat, or AI conferences.
Experience with Cloud Security (AWS/Azure/GCP) and Infrastructure-as-Code scanning.
Experience fine-tuning models for specific code-generation or security tasks.
The Core Stack:
Systems: 3+ years of experience with Golang and Linux Kernel development (eBPF or Kernel modules).
AI/ML: Hands-on experience with LLMs, prompt engineering, and Python-based data analysis.
Security: Deep understanding of SAST/SCA tools (e.g., SonarQube, Bearer, Snyk) and Container Security (Docker, K8s, Trivy).
Technical Qualifications:
Strong knowledge of Linux systems design, networking, and OS internals.
Proficiency in Python (for AI research) and Go/C (for Agent development).
Experience in analyzing container build pipelines and identifying vulnerability origins.
Ability to distill complex topics (both kernel-level and AI-level) for diverse audiences.
Bonus Points:
Experience with Rego/Open Policy Agent (OPA).
Publications or presentations at venues like KubeCon, Black Hat, or AI conferences.
Experience with Cloud Security (AWS/Azure/GCP) and Infrastructure-as-Code scanning.
Experience fine-tuning models for specific code-generation or security tasks.
This position is open to all candidates.
