4 дня назад
Staff ML Engineer (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff ML Engineer (AI): Building and deploying machine-learning models, inference engines, and AI features spanning GPU infrastructure, NPU silicon, embedded systems, and security with an accent on hardware-aware optimization, production ML, and end-to-end AI silicon development. Focus on extending NPU cores, optimizing latency, memory, and power, building verification harnesses, and integrating AI across firmware, hardware, and QA.
Location: Taipei, Taiwan
Company
develops AI-enhanced security processors and silicon-rooted security and management chips for AI data center infrastructure, combining platform security, BMC firmware, and on-chip AI.
What you will do
- Optimize training and inference performance across GPU and AI-accelerator infrastructure, including MLOps pipelines.
- Design, train, evaluate, and productionize machine-learning models for deep learning, LLM, computer vision, and recommendation use cases.
- Extend NPU cores and optimize inference engines and serving runtimes for hardware constraints such as latency, memory, and power.
- Develop AI functionality across BMC firmware, embedded Linux, and RTOS environments.
- Build automated verification, hardware bring-up, and manufacturing-test harnesses for AI-assisted RTL and hardware development.
- Apply machine learning to security, including log and intrusion analysis, penetration testing, and firmware or hardware security.
Requirements
- 5–7+ years of hands-on AI/ML experience.
- Master’s degree required; PhD preferred.
- Experience with GPU clusters, distributed training, inference-serving optimization, and MLOps pipelines.
- Experience designing, training, evaluating, deploying, and maintaining ML models, including feature engineering and data pipelines.
- Experience with AI-chip or hardware-aware ML, including inference optimization, model quantization, or adapting architectures to chip constraints.
- Deep hands-on expertise in at least two areas: NPU or AI accelerators, systems software, inference runtimes, test and verification harnesses, or cybersecurity.
Culture & Benefits
- Collaborative environment focused on innovation, persistence, curiosity, and solving real-world problems.
- Cross-functional work with RTL, hardware, firmware, and QA teams.
- Opportunities to work across the full AI silicon lifecycle, from model training through deployment and monitoring.
- Environment supporting continuous learning, mutual support, and diverse teams.
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