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全职 AICS SG - Machine Learning Engineer 工作, 薪水, ASUS 公司招聘中 - Ricebowl

AICS SG - Machine Learning Engineer

Undisclosed

Singapore

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工作地点

  • Singapore

职位描述

岗位职责

AICS (*************) is a newly formed organization incubating applied AI services that can leverage ASUS' large portfolio of hardware products to unlock a new generation of smart products and services. The team is building and deploying deep technologies in Data Analytics, Speech Recognition, Natural Language Understanding, and Computer Vision to help accelerate the transformation towards an AI-powered future.


We are currently hiring for multiple positions across Software Engineering, AI/ML, and Data Science. Our engineers are deploying new models and applications that are deployed from the edge to the cloud. We are excited to hear from you!


Machine Learning Engineer

Responsibilities:


  • Design and implement appropriate ML algorithms and tools, select appropriate datasets, benchmark models, and perform statistical analysis and fine-tuning.
  • Keep models up to date by incorporating continuous learning into the system.
  • Work with the AI Research team to transform prototypes into production models using best practices for model development, management, and maintenance.
  • Work closely with an entrepreneurial team of experienced product managers and software engineers to successfully ship software products and continue to grow our business.
  • Research new ideas in machine learning and identify product opportunities for applying them.


Qualifications:


  • Bachelor’s degree in computer science or related field with at least 3 years of relevant experience.
  • Proven experience in designing, training and deploying machine learning models in a production environment.
  • Experience with LLM SFT/DPO is preferred.
  • Programing experience with Python/C++ or equivalent.
  • Development experience with ML frameworks and libraries.
  • Strong background in NLP or CV, preferably in the medical / healthcare domain
  • Ability to work independently and ramp up to new areas quickly.

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