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Android Malware Reverse Engineer Jobs in Santa Cruz, CA

GIAC Reverse Engineering Malware (GREM) * GIAC Defending Advanced Threats (GDAT) * GIAC Cyber Threat Intelligence (GCTI) * Certified Information Systems Security Professional (CISSP) * Other ...

Senior Security Engineer, Digital Forensics

San Jose, CA ยท On-site

$134K - $184K/yr

... malware capabilities. * Experience with security of two or more operating systems e.g. Android ... Experience with reverse engineering or firmware analysis About the job There's no such thing as a ...

New

Software Engineer 3

San Jose, CA ยท On-site +1

$67.50 - $90.50/hr

Java (J2EE, concurrency), Python (ML/DS), JavaScript/TypeScript (React, Node.js), Kotlin (Android ... Secure coding (validation, memory safety); penetration testing (OWASP ZAP); reverse engineering ...

Android Malware Reverse Engineer information

See Santa Cruz, CA salary details

$26.6K

$159.2K

$223.4K

How much do android malware reverse engineer jobs pay per year?

As of Aug 1, 2026, the average yearly pay for android malware reverse engineer in Santa Cruz, CA is $159,201.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,900.00 and $184,700.00 per year, depending on experience, location, and employer.

What does an Android Malware Reverse Engineer do?

An Android Malware Reverse Engineer analyzes malicious software targeting Android devices to understand how it works, identify its behavior, and develop ways to detect or remove it. They use specialized tools to decompile and inspect code, examine app permissions, and trace network activity. Their findings help improve mobile security, assist law enforcement, and protect users from cyber threats. This role often requires strong programming skills, familiarity with Android internals, and knowledge of cybersecurity techniques.

What are some typical challenges faced by Android Malware Reverse Engineers in their daily work?

Android Malware Reverse Engineers often encounter obfuscated or encrypted code, which makes it challenging to analyze malicious software efficiently. They must stay updated with evolving malware techniques and anti-analysis strategies that threat actors deploy. Collaborating closely with threat intelligence and security operations teams is crucial, as findings often contribute to broader security defenses. The role requires patience, attention to detail, and strong problem-solving skills to effectively dissect and understand complex malware behaviors.

What is the difference between Android Malware Reverse Engineer vs Mobile Security Analyst?

AspectAndroid Malware Reverse EngineerMobile Security Analyst
CredentialsKnowledge of reverse engineering, malware analysis, programming skillsSecurity certifications (e.g., CISSP, CEH), understanding of mobile security
Work EnvironmentResearch labs, cybersecurity firms, or in-house security teamsCorporate security teams, consulting firms, or government agencies
Industry UsageFocus on analyzing malicious Android apps and malwareBroader mobile security issues, including vulnerabilities and threat mitigation
Search & Comparison IntentUnderstanding technical malware analysis rolesBroader mobile security roles and responsibilities

While both roles involve mobile security, the Android Malware Reverse Engineer specializes in dissecting malicious Android applications to understand and mitigate threats. The Mobile Security Analyst has a broader focus on overall mobile security strategies, including vulnerability assessments and threat management across platforms.

What are the key skills and qualifications needed to thrive as an Android Malware Reverse Engineer, and why are they important?

To thrive as an Android Malware Reverse Engineer, you need expertise in malware analysis, reverse engineering, programming (Java, Kotlin, C/C++), and strong knowledge of Android OS internals, often supported by a degree in computer science or a related field. Familiarity with tools such as IDA Pro, Ghidra, Android Studio, Wireshark, and mobile security frameworks, as well as certifications like GIAC Reverse Engineering Malware (GREM), is typically required. Analytical thinking, attention to detail, persistence, and effective communication are crucial soft skills for excelling in this role. These skills enable accurate detection, analysis, and mitigation of threats to protect Android devices and users from evolving malware risks.
What job categories do people searching Android Malware Reverse Engineer jobs in Santa Cruz, CA look for? The top searched job categories for Android Malware Reverse Engineer jobs in Santa Cruz, CA are:
What cities near Santa Cruz, CA are hiring for Android Malware Reverse Engineer jobs? Cities near Santa Cruz, CA with the most Android Malware Reverse Engineer job openings:

Principal Researcher, Botnet & DDoS Threats

Osv_a10networks

San Jose, CA โ€ข On-site

$200K - $215K/yr

Full-time

Re-posted 25 days ago


Job description

Principal Researcher, Botnet & DDoS Threats

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,tsharkAny.run, Joe Sandbox, Cuckoo (or equivalent) custom detonation lab infrastructure honeypot infrastructure MalwareBazaar,VirusTotal macOS or Linux

AI Use Guidelines for Interviews:Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.

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.#LI-AN1 - Hybrid