Principal Researcher, Botnet & DDoS Threats

A10 Networks, Inc; San Francisco (CA); Hybrid; USD 200,000 - 215,000; Full time

Job summary

A10 Networks, Inc. is seeking a Reverse Engineer specializing in IoT botnet malware. This role involves reverse engineering, dynamic malware analysis, and collaborating with product engineering on detection capabilities. You will engage directly with customers and lead external research efforts. The ideal candidate should possess strong knowledge in binary reverse engineering, proficiency in Python and Go, and a solid understanding of network protocols. The position offers a hybrid work model and a competitive salary in the range of $200,000 - $215,000.

Qualifications

• Strong foundation in binary reverse engineering with Ghidra or IDA.
• Hands-on experience with dynamic malware analysis in lab environments.
• Working proficiency in Python and Go.

Responsibilities

• Reverse engineer IoT botnet malware to understand attack behavior.
• Perform dynamic malware analysis in isolated environments.
• Design detection and mitigation approaches from malware analysis.

Skills

Binary reverse engineering

Dynamic malware analysis

Proficiency in Python

Understanding of network protocols

Communication of technical findings

Tools

Ghidra

Python 3

Go

Scapy

Job description

The DDoS threat landscape has crossed a threshold. Botnets like Aisuru and Kimwolf—comprising millions of compromised Android TV and IoT devices and capable of attacks exceeding 24 Tbps and 9 billion packets per second—are no longer edge cases. They are the baseline.

Defeating these threats requires more than external observation. It requires deep visibility into how they are built, how they execute on the wire, and what that means for the systems designed to stop them.

This role sits at the intersection of binary exploitation research and real‑world defensive impact. You will reverse engineer active IoT botnet malware, translate findings into detection logic and packet‑level attack signatures, and work across engineering, product, and research to ensure insights directly improve detection and customer defense.

What You Will Do

• Reverse engineer IoT botnet malware families (Mirai lineage, Go-based L7 flooders, multi‑architecture binaries) to understand attack behavior at the implementation and network level. You will reconstruct command structures, decode obfuscation, recover control flows from stripped binaries, and build precise models of how attacks manifest on the wire.
• Perform dynamic malware analysis in sandboxed and purpose‑built lab environments to validate static analysis and observe runtime behavior.
• Design and contribute to novel detection and mitigation approaches based on malware internals and traffic behavior.
• Collaborate with AI/ML teams to integrate automated analysis into research workflows. This is not passive tool usage—you will actively shape how automation is applied to real malware analysis problems.
• Partner with product engineering to translate research into shipped detection capabilities.
• Lead external‑facing research: threat reports, technical blogs, and conference presentations. At principal level, you own the narrative and direction of research output.
• Engage directly with customers in post‑incident analysis, architectural guidance, and strategic threat briefings—clearly explaining both attacker behavior and defensive actions.
• Work alongside senior researchers focused on IoT botnets and large‑scale DDoS systems, contributing to and benefiting from a deeply technical peer environment.

What You Need

• Strong foundation in binary reverse engineering using tools such as Ghidra or IDA, including static analysis across multiple architectures and experience with stripped binaries and compiler‑generated code; you should be comfortable working close to raw assembly and control flow, not dependent on tooling abstraction.
• Hands‑on experience with dynamic malware analysis in sandbox or isolated lab environments, using runtime observation to validate and extend static findings.
• Working proficiency in Python and Go.
• Strong understanding of network protocols at the implementation level, including the ability to interpret PCAPs and reconstruct protocol behavior.
• Familiarity with DDoS botnet architectures (e.g., Mirai lineage or equivalent), ideally with direct analysis of binaries rather than secondary reporting. Experience tracking variant evolution across malware families is a strong plus.
• Ability to communicate complex technical findings clearly across engineering, product, and customer audiences; at this level, communication quality is a core part of technical impact.

Nice to Have

• Experience with high‑performance packet processing or mitigation systems at the network and transport layers.
• Experience analyzing Go binaries in depth.
• Exposure to malware source code.
• Experience applying ML‑assisted or vector‑based approaches to malware classification, clustering, or lineage attribution.

Tools & Environment

• Ghidra (headless + GUI), Capstone, GoReSym.
• Python 3, Go, Scapy, tshark.
• Any.run, Joe Sandbox, Cuckoo (or equivalent).
• Custom detonation lab infrastructure.
• Honeypot infrastructure.
• MalwareBazaar, VirusTotal.
• macOS or Linux.

Targeted compensation guideline: $200,000 - $215,000. Compensation will vary based on number of factors, including market demand for specific skills, role type, job level, and individual qualifications. Final salary offers are determined by considerations including, but not limited to, subject matter expertise, demonstrated skill level, relevant experience, geographic location, education, certifications, and training.

A10 Networks is an equal opportunity employer and a VEVRAA federal subcontractor. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. A10 also complies with all applicable state and local laws governing nondiscrimination in employment.

Hybrid