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[新加坡]知名自动驾驶智能公司，现招聘资深开发工程师 C++（有嵌入式经验）， base 新加坡，国际化团队，要求中英文良好，国际化办公环境。
sophiayao · 2022-11-23 10:01:12 +08:00 · 757 次点击这是一个创建于 766 天前的主题，其中的信息可能已经有所发展或是发生改变。
知名自动驾驶方向智能公司，国际化团队，现招聘资深开发工程师 C++（有嵌入式经验），base 新加坡 预算折合人民币 40-50K 月薪，欢迎中英文良好，有来新加坡发展意愿的详聊：微信：sophia_liu611
Full Job Description
We are looking for an outstanding self-motivated engineer working with the multi-disciplinary team of scientists and engineers to conduct R&D in the fields of video analytics, machine learning, autonomous driving perception and AI. We have active research and development projects where we design, integrate, test, and deploy state-of-the-art systems for a range of applications, including image and video analytics, artificial intelligence, scene understanding, perception for autonomous systems.
Responsibilities
o Deliver/release algorithms in terms of SDK library on product-specific platforms.
o Port algorithms to different platforms, evaluate and optimize performance.
o Work on parallel computing design by allocating CPU cores and system resources properly.
o Work closely with the algorithm research team and system software engineering team.
o Bridge the gap between algorithm theory and commercial production level software deliverable.
o Analyze, implement, profile, and optimize the company’s machine learning and image processing algorithms.
Qualifications
o MS or Ph.D. in Electrical, Computer Engineering or Computer Science or related with 2-5 years’ equivalent experience.
o Fluent in both English and Mandarin.
o Fluent in C/C++, Python, and script languages.
o Solid understanding of computer architecture, CPU scheduler, and parallel computing.
o Experienced in the implementation of computer vision and machine learning algorithms.
o Good communication, documentation, and teamwork.
It’s preferred if you have one or more of the following:
o Knowledge of the theory and algorithms of computer vision, machine learning and deep learning.
o Experienced on an embedded platform, ARM processors, DSP, OpenCL, OpenMP, CUDA, Neon.
o Experienced developing and deploying models for edge devices.
o Experienced with building infrastructure and tools for applied machine learning, including evaluation/validation techniques, and performance optimaztion.
Full Job Description
We are looking for an outstanding self-motivated engineer working with the multi-disciplinary team of scientists and engineers to conduct R&D in the fields of video analytics, machine learning, autonomous driving perception and AI. We have active research and development projects where we design, integrate, test, and deploy state-of-the-art systems for a range of applications, including image and video analytics, artificial intelligence, scene understanding, perception for autonomous systems.
Responsibilities
o Deliver/release algorithms in terms of SDK library on product-specific platforms.
o Port algorithms to different platforms, evaluate and optimize performance.
o Work on parallel computing design by allocating CPU cores and system resources properly.
o Work closely with the algorithm research team and system software engineering team.
o Bridge the gap between algorithm theory and commercial production level software deliverable.
o Analyze, implement, profile, and optimize the company’s machine learning and image processing algorithms.
Qualifications
o MS or Ph.D. in Electrical, Computer Engineering or Computer Science or related with 2-5 years’ equivalent experience.
o Fluent in both English and Mandarin.
o Fluent in C/C++, Python, and script languages.
o Solid understanding of computer architecture, CPU scheduler, and parallel computing.
o Experienced in the implementation of computer vision and machine learning algorithms.
o Good communication, documentation, and teamwork.
It’s preferred if you have one or more of the following:
o Knowledge of the theory and algorithms of computer vision, machine learning and deep learning.
o Experienced on an embedded platform, ARM processors, DSP, OpenCL, OpenMP, CUDA, Neon.
o Experienced developing and deploying models for edge devices.
o Experienced with building infrastructure and tools for applied machine learning, including evaluation/validation techniques, and performance optimaztion.
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