4 дня назад
Applied AI Engine Engineer in Test (Content Inspection, Shisa)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Applied AI Engine Engineer in Test (Content Inspection, Shisa): Testing and evaluating Trend Micro virus-scan and advanced threat-scan engines with an accent on C/C++/Perl/Python automation, threat research, and AI-assisted QA workflows. Focus on generating malicious and benign content samples, validating detection accuracy and AI-driven classification logic, and improving coverage through agent-based testing tools.
Location: Taipei, Taiwan
Company
develops cloud and enterprise cybersecurity software, including virus-scan and advanced threat-scan engines.
What you will do
- Test and evaluate the quality of virus-scan and advanced threat-scan engines used across projects.
- Write and review evaluation code using C, C++, Perl, and Python.
- Analyze emerging threats and research detection challenges.
- Collaborate with cross-functional teams to design threat-detection patterns, review technical solutions, and deliver scan engines.
- Create and maintain automation scripts, processes, and systems for testing.
- Use AI-native tools such as Claude Code, GitHub Copilot, and Cursor to design, automate, and optimize test workflows.
Requirements
- Bachelor’s or master’s degree in Computer Science or a related field.
- Experience with C, C++, Perl, or Python.
- Experience with the Windows operating system.
- Experience integrating AI tools into QA activities, including test-case generation, log analysis, defect triage, or code review.
- Experience building repeatable AI-assisted test workflows, including prompt engineering and context management.
- Good English communication skills required.
Nice to have
- Experience testing content inspection scenarios, including malicious and benign sample generation and detection-accuracy evaluation.
- Experience with MCP servers, AI Agent Skills, or other agent-based tools.
- Experience standardizing AI-assisted QA workflows, documenting practices, and enabling teammates.
- Exposure to AI-powered detection or classification models and regression testing of their outputs.
Culture & Benefits
- AI-first QA transformation aligned with ’s 2026 strategy.
- Emphasis on experimentation, continuous learning, and applying AI to real-world QA challenges.
- Encouragement to explore new tools and share knowledge with the team.
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