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Salary
≈ $166k – $352k per year (Estimated)
Location
In office (San Jose)
Seniority
Architect

Confirmed on the employer's own hiring board on Sep 24, 2026. First seen by Alion on Oct 13, 2025.

Overview
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MediaTek Inc. is a global fabless semiconductor company specializing in system-on-chip (SoC) architectures and digital multimedia processing technologies. The enterprise designs and markets integrated circuits for smartphones, smart home devices, IoT hardware, wireless connectivity solutions, automotive electronics, and custom AI chips for edge and cloud applications. Headquartered in Hsinchu, Taiwan, the organization operates an international network of research, development, and sales offices serving original equipment manufacturers (OEMs) and technology brands worldwide.
MediaTek Incorporated is a global fabless semiconductor company that enables nearly 2 billion connected devices a year. We are a market leader in developing innovative systems-on-chip (SoC) for mobile device, home entertainment, connectivity and IoT products. MediaTek is the number one Wi-Fi supplier across broadband, retail routers, consumer electronics devices and gaming, and its Wi-Fi 6 chipsets are powering the latest networking equipment for faster computing experiences.

MediaTek’s Automotive Platform Business Unit is Hiring a Principal Engineer - Generative AI & Edge Systems, Automotive.

In this role, you will:

(1) Research and develop Generative AI and deep learning-based systems for in-vehicle domains - Infotainment, Speech, UI, and multimodal ADAS - with a focus on architectures deployable on resource-constrained edge hardware.

(2) Lead integration of GenAI frameworks and toolchains optimized for automotive edge compute, driving adoption of cutting-edge on-device AI techniques from research into production.

(3) Partner with Silicon and Software engineering teams at HQ (Taiwan) and automotive customers to define and deliver technical solutions for GenAI and LLM requirements, with edge feasibility as a core design constraint from the start.

(4) Own end-to-end LLM optimization for on-device deployment on GPUs and NPUs - profiling models, identifying bottlenecks, and driving improvements at both the model level (quantization, distillation, pruning) and framework level to maximize inference performance within embedded compute and power budgets.

(5) Track industry and academic advances in edge AI and efficient LLM techniques, translating relevant breakthroughs into practical, deployable improvements for in-vehicle systems.

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