AE 產品應用工程師 - Edge Computing and Edge AI (Embedded/ 林口園區)

Job Summary

We are looking for a skilled Application Engineer to join our team, focusing on Edge Computing and Edge AI solutions. You will provide pre-sales and post-sales technical support, and manage system operations such as software drivers, firmware, and platform integration. Your role includes diagnosing and resolving issues, working with various departments, and offering feedback to customers.

If you are passionate about technology and enjoy a collaborative environment, welcome to join us!

Key Responsibilities

◆ Provide pre-sales and post-sales technical support for products.

◆ Write, maintain, and update product technical documentation.

◆ System operation, including software driver, firmware, and platform integration.

◆ Troubleshoot and analyze issues, collaborate across departments to clarify and resolve issues, and provide feedback to customers.

◆ Test and validate new products. Provide product training and technology transfer and assist in product promotion.

◆ Be able to travel for short-term assignments when necessary.

Requirements

◆ Essential Skills:

-Problem-solving: Ability to analyze problems, propose solutions, and validate results.

-Teamwork: Ability to collaborate with experts from various fields to accomplish project goals.

-Communication: Ability to articulate ideas clearly and communicate effectively with others.

-Continuous Learning: Willingness to continuously learn new knowledge and skills in the rapidly evolving field of edge computing.

◆ Technical Skills:

-Embedded Systems Development: Proficient in various embedded system architectures, operating systems (such as Linux, RTOS), hardware platforms (such as ARM, x86), and related development tools.

-Software Development: Expert in programming languages like C/C++ and Python, with familiarity with relevant development frameworks and tools.

-Machine Learning and Deep Learning: Experienced in deep learning frameworks (e.g., TensorFlow, PyTorch) and model optimization techniques.

-Hardware Acceleration: Proficient in hardware accelerators like GPUs, NPUs and FPGAs, and capable of deploying AI models onto these accelerators

-Proficient in managing Azure Virtual Machines (VMs), Azure App Services, and Extended Support.