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R&D Technical Manager

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Onsite

R&D Technical Manager

FILPAL (M) SDN BHD

R&D Technical Manager

Skills

RMachine LearningAI

25 days ago

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Important information


Contract type:

Permanent

Location:

George Town, Penang, Malaysia

Work mode:

Onsite

Published on:

28 September 2026

What they need


Lead the R&D team across RF hardware, software, AI and system engineering. Define and run a structured research-to-product programme in C-UAS covering SDR, radar, algorithms, and technology transfer into fielded systems. Build the organisation, processes and technical bar.

  1. R&D leadership
  • Lead a multi-disciplinary R&D organisation spanning RF/microwave hardware, SDR, embedded and application software, AI/ML, signal processing, and system integration.

  • Set technical strategy, roadmaps, architecture standards and design reviews across product lines, with C-UAS as the flagship programme.

  • Build and coach team leads; hire, retain and develop engineers; establish a culture of rigorous engineering, field-first thinking and delivery.

  • Translate MD/CEO business priorities into a funded, sequenced R&D plan with clear milestones, risk registers and go/no-go gates.

  1. C-UAS technology programme
  • Charter a multi-year C-UAS research and productization programme covering: SDR platforms and RF front-ends; RF direction finding and spectrum intelligence; radar sensing and multi-sensor fusion; detection, classification and tracking algorithms; protocol/telemetry decoding; and mitigation coordination.

  • Define the system architecture for sensor layer, processing layer and effector interfaces.

  • Drive technology transfer from research, partners, universities and suppliers into implementable, testable, manufacturable product — not demos that die in the lab.

  • Establish evaluation methodology: lab characterisation, outdoor trials, false-alarm / detection metrics, latency, robustness in congested spectrum, and mission-scenario test plans.

  1. Software, AI and algorithms
  • Direct signal-processing and AI workstreams: detection, classification tracking, sensor fusion, and operator decision support.

  • Oversee software architecture for real-time pipelines, edge vs. server compute, data/IQ management, offline protocol libraries

  • Set standards for model training data, labelling, evaluation, versioning, and field update — so algorithms can be transferred and maintained, not locked in one engineer’s notebook.

  1. System engineering and productization
  • Connect R&D to NPI, manufacturing, test, field integration and after-sales so prototypes become deployable products

  • Support customer technical engagements, demonstrations, proposals and acceptance tests with credible engineering, not slideware.

  1. Partnerships, research network and technology transfer
  • Build and manage research and industrial partnerships

  • Represent in technical forums, standards discussions and selected government / defence engagements.

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