OpenNash Jobs
MediaTek · HsinChu

R&D and Information Security Risk Management Engineer

HsinChuResearch & Science

Listed on MediaTek’s official careers site. Listed as of October 23, 2026. Check that it is still open and apply there.

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Job description

  • Perform internal audits over the IC design R&D cycle, covering product/customer requirements, system specification, architecture design, hardware design, software design, HW/SW integration, verification/validation, tape-out, software release, silicon bring-up, mass production introduction, version maintenance, and customer feedback processes.
  • Evaluate the design and operating effectiveness of key R&D controls
  • Support the development of R&D risk and control matrices, mapping R&D risks to control activities, data sources, audit test rules, and audit evidence to establish a traceable, measurable, and continuously monitorable audit framework.
  • Analyze R&D process and data risks to identify exceptions that may impact product quality, project schedule, customer commitments, intellectual property protection, information security, compliance, and business operations.
  • Conduct audits over R&D data and access controls
  • Design audit test logic and data analytics rules.
  • Collaborate with RD, IT, Information Security, Data Governance, Legal, and process owners to validate data sources, data quality, data lineage, permission boundaries, remediation plans, and issue closure status.
  • Prepare audit workpapers, risk analysis reports, and management reports, summarizing major risks, control effectiveness, remediation progress, repeat issues, overdue actions, and AI implementation results to support audit management, Audit Committee, or Board-level oversight.
  • Assist in developing and Audit Agents, using AI Agents to collect, cleanse, and integrate R&D flow data, and to enable daily risk monitoring, exception detection, evidence creation, and audit workpaper support.
  • Review AI / Audit Agent-generated audit findings to ensure each finding is supported by an evidence ID, data source, test logic, human review record, and sufficient audit evidence, following the principle that AI supports analysis while humans own audit conclusions. #LI-YT1

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