The XDR capabilities of Trend Micro’s Vision One platform provide context-aware investigation, recording, and reporting of system-level activities across multiple security layers — AI App, endpoint, servers, cloud workloads, email, and networks. We’re evolving how the team builds software. This isn’t a role defined by a technology layer — it’s defined by how you think and work. AI is your primary tool for accelerating the full engineering cycle, and system thinking is how you use it responsibly. You’ll start with a domain you’re strong in and grow from there.
[Your Mission]
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Start with one or two anchor domain — backend, frontend, or DevOps — and go deep. Over time, use AI as a bridge to build working knowledge across all three, not to replace specialists, but to collaborate without walls.
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Invest in understanding the product domain and user workflows deeply enough to translate requirements into precise system specs — using BDD/TDD as the shared language between human intent and AI generation.
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Integrate AI tools (e.g., GitHub Copilot, Claude Code) throughout the development lifecycle — from spec writing and test generation to code review and debugging — rather than using them as occasional shortcuts.
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Own the full engineering cycle: from requirement to spec, AI-assisted implementation, architecture-aware review across frontend, backend, QA, and DevOps, through to data-driven validation that what we shipped actually solves the problem.
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Use data as a feedback loop — monitor service signals (reliability) and track feature usage to validate outcomes, and feed those insights back into the next iteration.
[Your Entry Point]
Hiring process
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Pick the domain you’re strongest in. This is where you’ll go deep first.
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Backend — Experience in Java and AWS cloud services.
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Frontend — Experience in browser JavaScript development, including React and core web technologies (HTML/CSS/JS).
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DevOps — Experience in Docker/K8S/CI/CD and cloud infrastructure operations.
[Required — All Tracks]
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Bachelor’s degree or above in computer science or related fields.
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Hands-on experience with AI-native development tools (e.g., Cursor, GitHub Copilot, Claude Code) in a real project or side project.
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Ability to write clear BDD/TDD specs that scope AI-assisted development — understanding that the quality of specs determines the quality of AI output.
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Ability to provide effective context when working with AI — understanding that output quality depends on input quality.
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Habit of critically reviewing AI-generated code rather than adopting it directly, with awareness of system-wide concerns such as API contracts, data models, and security boundaries.
[Big Plus]
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Working knowledge beyond your anchor domain — frontend, backend, or DevOps.
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Familiar with cloud infrastructure such as networking, security, and computing stack.
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Experienced in operating products/services at large scale.
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Deep understanding of networking and Linux operating systems.
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Understanding of agent frameworks or common agent design patterns .
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Familiarity with harness engineering techniques to guide AI toward higher-quality outputs.