Software Engineer Large Model System Graduate (Machine Learning Sys-US) - 2024 Start (BS/MS)

Thegradcafe•Jackson, MS

8 days ago

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Successful candidates must be able to commit to a start date before the end of 2024
Please state your availability and graduation date clearly in your resume
Minimum Requirements: Graduate with a background in Computer Science, related technical field or equivalent industrial experience
Master distributed, parallel computing principles; know the recent advances in computing, storage, networking, and hardware technologies
Familiar with the state-of-the-art machine learning algorithms and mainstream platforms (e.g., Tensorflow, Pytorch, MxNet)
Master at least one or two programming languages in Linux environment such as C/C++, Go, Python, etc
Familiar with NLP, CV-related algorithms, and technologies, and experienced in large model training
Experience in GPU based high-performance computing
Demonstrated a related technical experience from previous internship, work experience, coding competitions, or publications
Curiosity towards new technologies and entrepreneurship
High levels of creativity and quick problem-solving capabilities

As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas, and explore limitless growth opportunities

The team is also responsible for the research and development of hardware acceleration technologies for cloud computing, via technologies such as distributed systems, compilers, HPC, and RDMA networking
Responsibilities: Responsible for the machine learning system development of the company's large-scale models, researching new applications and solutions of related technologies in areas such as search, recommendation, advertising, content creation, conversation, and customer service, meeting the growing demand for intelligent interaction from users, and comprehensively improving users' lifestyles and communication methods in the future world
Responsible for the design and development of the architecture of large-scale machine learning systems, solving technical difficulties such as high concurrency, high reliability, and high scalability of the system
Covering various sub-directions of machine learning system, including resource scheduling, model training, model inference, data management, and workflow
Iterate and develop the system using customer-driven scenarios

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