Senior Engineer - AI Agentic Fuzzing & Linux Security

Company: Qualcomm India Private Limited Job Area: Engineering Group, Engineering Group > Software Engineering General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs. Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces. Minimum Qualifications: • Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field. • 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc. Senior Engineer – AI Agentic Fuzzing & Linux Security About the Role We are looking for a Senior Security Engineer to contribute to the area of Linux platform security, with a focus on AI-driven automated fuzzing and continuous security assurance across embedded and mobile platforms. The person will be responsible for designing and deploying intelligent fuzzing infrastructure, developing fuzzing harnesses using agentic AI frameworks, performing security risk assessments, and conducting secure code reviews for Linux kernel and driver stacks. The ideal candidate possesses a strong security-first mindset, hands-on experience with industry-leading fuzzers, and a passion for leveraging AI/LLM-based agentic frameworks to scale and automate vulnerability discovery. Key Responsibilities • Design, develop, and deploy AI-agent-driven fuzzing harnesses targeting Linux kernel subsystems, device drivers, and system interfaces using frameworks such as LangChain, Agno, AutoGen, or similar agentic orchestration tools. • Build and maintain continuous fuzzing pipelines (e.g., OSS-Fuzz, QSS-Fuzz, ClusterFuzz) integrated into CI/CD workflows for automated vulnerability detection. • Develop and tune fuzzing campaigns using popular fuzzers including Syzkaller, libFuzzer, AFL++ and custom mutation strategies. • Perform security risk assessments and threat modeling on Linux platform components, kernel modules, and firmware interfaces. • Conduct thorough secure code reviews to identify memory corruption, privilege escalation, race conditions, and other vulnerability classes (CWEs). • Triage and analyze crash reports, root-cause security bugs, and coordinate patch development with owning teams. • Research and prototype LLM-assisted harness generation, enabling near-autonomous fuzzing target creation for new codebases. • Collaborate with platform, kernel, and security teams to integrate security requirements across the Security Development Lifecycle (SDLC). Ideal Candidate Profile The ideal candidate also needs to have: • Strong knowledge of Linux kernel internals — memory management, scheduling, IPC, syscall interface, and kernel security mechanisms (LSM, seccomp, namespaces). • Deep expertise in Linux device driver development (character devices, platform drivers, PCIe, USB, UART, etc.) and driver debugging. • Hands-on experience with agentic AI frameworks and the ability to build autonomous agents that reason over codebases to generate and evolve fuzz targets. • Experience deploying continuous fuzzing infrastructure at scale — job scheduling, corpus management, crash deduplication, and coverage-guided optimization. • Proficiency with Syzkaller — syscall description writing, syzbot integration, repro analysis, and custom grammar extension. • Strong secure code review skills with ability to identify vulnerability patterns in C/C++ codebases. • Familiarity with firmware security — UEFI, BIOS, TrustZone/TEE, and secure boot chains is a plus. • Strong problem-solving, debugging, and cross-team communication skills. • Ability to coordinate across stakeholders and manage security deliverables across multiple workstreams. Minimum Qualifications • Bachelor's degree in Engineering, Computer Science, Information Security, or related field and 2+ years of Software/Security Engineering experience.ORMaster's degree in Engineering, Computer Science, or related field and 2+ years of Software/Security Engineering experience.ORPhD in Engineering, Computer Science, Information Security, or related field. • 2+ years of hands-on experience with programming languages such as C, C++, Python, and scripting for automation. • Demonstrated experience with Linux kernel development or security research. Preferred Qualifications • ✅ 2+ years of Security Engineering experience with a focus on vulnerability research or offensive security tooling. • ✅ Hands-on experience developing fuzzing harnesses for complex kernel or driver targets. • ✅ Proficiency with Syzkaller and/or libFuzzer/AFL++ in production fuzzing campaigns. • ✅ Experience building or contributing to AI/LLM-based code analysis or automated testing pipelines. • ✅ Knowledge of ARM/RISC-V processor architectures, assembly language, and low-level debugging (JTAG, GDB, KGDB). • ✅ Familiarity with static analysis tools (Klocwork, CodeQL, Semgrep) and integrating them into security pipelines. • ✅ Experience with CVE research, bug bounty programs, or publishing security advisories. • ✅ Understanding of embedded OS security, RTOS security hardening, and firmware attack surfaces. • ✅ Exposure to OSS-Fuzz / ClusterFuzz infrastructure management and coverage reporting. • ✅ Strong written and verbal communication skills with the ability to present security findings to both technical and non-technical audiences. 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