Senior Software Engineer — Python exp is a must

Responsibilities

• Develop, optimize, and maintain static analysis capabilities, including PE and Authenticode signature validation, YARA scanning, Office macro analysis, PDF inspection, archive analysis, and cloud-based hash reputation lookups.
• Manage the machine learning inference pipeline across both products, including model integration, tuning, deployment, and version control within production services.
• Own and enhance the dynamic analysis environment, including Windows VM lifecycle management on KVM/libvirt, guest configuration, behavioral telemetry collection, and support for additional operating systems.
• Read, troubleshoot, enhance, and maintain C/C++-based analysis components that support platform detection capabilities.
• Improve detection effectiveness by developing new analysis engines, expanding file-type coverage, creating behavioral signatures, and increasing test coverage across detection workflows.
• Investigate and resolve production detection issues, including false positives, false negatives, engine failures, and sandbox-related issues.
• Participate in architectural discussions, design reviews, and code reviews to help maintain high engineering standards.
• Collaborate closely with cross-functional teams to continuously improve platform performance, reliability, and detection accuracy.

Requirements

• Minimum of 8 years of software engineering experience with a Bachelor's degree in Computer Science, Engineering, or a related field; alternatively, 6+ years with a Master's degree, 3+ years with a PhD, or equivalent professional experience.
• Strong Python development experience, including multithreaded programming and performance optimization.
• Working proficiency in C and C++, with the ability to read, debug, build, and modify compiled analysis components.
• Solid understanding of static file analysis techniques, including PE file structures, code-signing validation, YARA rules, Office macros, PDF formats, and archive file inspection.
• Experience deploying and maintaining machine learning inference solutions in production using frameworks such as PyTorch, LightGBM, scikit-learn, or ONNX, including feature engineering and model optimization.
• Familiarity with KVM/libvirt or similar virtualization technologies and a working knowledge of Windows internals, or the ability to quickly develop expertise in these areas.
• Experience with malware analysis, sandbox technologies, threat detection platforms, or cybersecurity software.
• Comfortable working in Linux environments, including process troubleshooting, log analysis, and shell scripting on Ubuntu or Debian-based systems.
• Strong written communication skills and the ability to collaborate effectively within a distributed, asynchronous team environment.

Cross-Team Coverage Expectations

• Contribute to a collaborative engineering environment where team members provide coverage across functional areas when needed.
• Provide maintenance-level support and first-line incident response outside primary areas of expertise, including Python-based services, distributed task processing systems, management APIs, and deployment infrastructure.
• Support operational continuity during teammate absences and partner with subject matter experts when deeper technical specialization is required.