We are seeking a Senior / Principal Security Researcher to join our high-caliber Security ML Team. Our team solves complex security challenges using disruptive technologies, AI/ML algorithms, and massive datasets to design and develop groundbreaking security solutions that transition directly from research into production-grade protection.
In this role, you will serve as the core security domain expert within a multidisciplinary team, providing essential security-side expertise to assist in the development of ML models, deeplearning models, and Small Language Models (SLMs). By translating complex malware behaviors and static file attributes into actionable logic, your research will directly shape the algorithms that protect millions of endpoints worldwide. If you are looking to autonomously drive high-impact research initiatives from inception to production and use data-informed critical thinking to influence advanced security modeling, we want to hear from you.
Key Responsibilities
The Bridge to the AI Ecosystem: Serve as the core security domain expert within a multidisciplinary team, translating complex malware behaviors and static file attributes into actionable features, labels, or heuristics-fueling AI algorithms and automated, data-driven security logic.
Telemetry & Sensor Optimization:Analyze raw endpoint telemetry (such as memory buffers, hook outputs, and IPC artifacts) to evaluate algorithm effectiveness and work with AI researchers to tweak or implement new models..
Deconstruct File Behavior: Look past the "black box" of machine learning by digging into executable formats (PE, ELF, Mach-O) and scripts to identify malicious behavior(e.g., packing, anomalous imports, obfuscation).
Drive Intelligent Protections:Take a security hypothesis, validate it via data analysis, and build Python-based proof-of-concepts that scale into production-grade detections-spanning machine learning classifiers to AI-informed automated rule generation.
Shape the Pipeline: Influence data pipeline strategies and model design by ensuring our datasets and automated logic accurately capture the ground truth of modern threat landscapes.
In this role, you will serve as the core security domain expert within a multidisciplinary team, providing essential security-side expertise to assist in the development of ML models, deeplearning models, and Small Language Models (SLMs). By translating complex malware behaviors and static file attributes into actionable logic, your research will directly shape the algorithms that protect millions of endpoints worldwide. If you are looking to autonomously drive high-impact research initiatives from inception to production and use data-informed critical thinking to influence advanced security modeling, we want to hear from you.
Key Responsibilities
The Bridge to the AI Ecosystem: Serve as the core security domain expert within a multidisciplinary team, translating complex malware behaviors and static file attributes into actionable features, labels, or heuristics-fueling AI algorithms and automated, data-driven security logic.
Telemetry & Sensor Optimization:Analyze raw endpoint telemetry (such as memory buffers, hook outputs, and IPC artifacts) to evaluate algorithm effectiveness and work with AI researchers to tweak or implement new models..
Deconstruct File Behavior: Look past the "black box" of machine learning by digging into executable formats (PE, ELF, Mach-O) and scripts to identify malicious behavior(e.g., packing, anomalous imports, obfuscation).
Drive Intelligent Protections:Take a security hypothesis, validate it via data analysis, and build Python-based proof-of-concepts that scale into production-grade detections-spanning machine learning classifiers to AI-informed automated rule generation.
Shape the Pipeline: Influence data pipeline strategies and model design by ensuring our datasets and automated logic accurately capture the ground truth of modern threat landscapes.
Requirements:
OS Internals Generalist:At least 3 years of experience in endpoint security research or offensive/defensive OS internals, with a strong conceptual grasp of Windows, Linux, or macOS subsystems (deep understanding of at least one).
Malware Analysis & Reverse Engineering:Practical experience with both dynamic and static analysis of malware, utilizing core reverse engineering concepts to parse file structures, unpack binaries, or understand execution flow.
System Diagnostics & Tracing: Hands-on experience utilizing low-level monitoring and tracing tools to dissect active system behavior (e.g., Sysinternals suite, strace, ptrace, lsof, netstat, ).
Malware Execution Mechanics:Solid understanding of common malware methodologies (MITRE ATT&CK), process injection, API hooking, and evasion techniques at the operating system layer.
Data-Informed Critical Thinking:Strong investigative skills using analytics and data interpretation to separate true security signals from normal OS noise.
Proficient Python Coding:Strong, hands-on Python skills for data manipulation, file parsing, and rapid prototyping.
Collaborative Communication:Excellent communication skills with the ability to explain low-level technical research results clearly to non-security disciplines (Data Scientists, MLOps, Product Managers).
End-to-End Project Ownership:Proven ability to autonomously drive complex research initiatives from inception to production, balancing independent deep-dives with cross-functional teamwork.
Preferred Qualifications
Native Code Comprehension: Proficiency in C / C++ / Rust (specifically for reading/interpreting agent code, hook structures, and raw memory buffers).
Data Scale: Experience with big data platforms (e.g., GCP), SQL, or related query languages.
OS Internals Generalist:At least 3 years of experience in endpoint security research or offensive/defensive OS internals, with a strong conceptual grasp of Windows, Linux, or macOS subsystems (deep understanding of at least one).
Malware Analysis & Reverse Engineering:Practical experience with both dynamic and static analysis of malware, utilizing core reverse engineering concepts to parse file structures, unpack binaries, or understand execution flow.
System Diagnostics & Tracing: Hands-on experience utilizing low-level monitoring and tracing tools to dissect active system behavior (e.g., Sysinternals suite, strace, ptrace, lsof, netstat, ).
Malware Execution Mechanics:Solid understanding of common malware methodologies (MITRE ATT&CK), process injection, API hooking, and evasion techniques at the operating system layer.
Data-Informed Critical Thinking:Strong investigative skills using analytics and data interpretation to separate true security signals from normal OS noise.
Proficient Python Coding:Strong, hands-on Python skills for data manipulation, file parsing, and rapid prototyping.
Collaborative Communication:Excellent communication skills with the ability to explain low-level technical research results clearly to non-security disciplines (Data Scientists, MLOps, Product Managers).
End-to-End Project Ownership:Proven ability to autonomously drive complex research initiatives from inception to production, balancing independent deep-dives with cross-functional teamwork.
Preferred Qualifications
Native Code Comprehension: Proficiency in C / C++ / Rust (specifically for reading/interpreting agent code, hook structures, and raw memory buffers).
Data Scale: Experience with big data platforms (e.g., GCP), SQL, or related query languages.
This position is open to all candidates.
