The Amazon Web Services Shanghai AI Lab is looking for a passionate, talented, and inventive staff in machine learning.
The Shanghai AI Lab pioneered the research and development of the Deep Graph Library (https://www.dgl.ai/), the world's leading platform that combines deep learning and structural data representation of graphs. This emerging new field holds promises to impact a wide surface of applications, both old and new, and is a fertile ground to push the frontier of AI research.
As an MLE, you will join a vibrant team with a diverse set of expertise in both machine learning and system research, and work with other world-class scientists and engineers in AWS Machine Learning group around the globe and across the boarders. Collaborate with data scientists, software engineers, and product managers to integrate ML solutions into production systems. Optimize existing ML models for improved performance, scalability, and efficiency. Mentor junior team members and contribute to the growth of the ML team.
With a particular emphasis on open-source projects, your work directly impacts researchers around the world. You should be comfortable diving deep into technical architectures and requirements, be able to quickly identify solutions to challenges discovered during development. A commitment to team work, hustle, and strong communication skills (to both business and technical partners) are absolute requirements.
BASIC QUALIFICATIONS
* M.S. degree in Computer Science and Engineering or related disciplines.
* 3+ years of software development experience in system infrastructure including but not limited to high performance computing, distributed system, compiler stack, GPU programming.
* Strong proficiency in Python and experience with ML frameworks such as PyTorch.
* Experience with deep learning and neural network architectures
* Proficiency in one of the following languages: C++ or Python.
PREFERRED QUALIFICATIONS
* Experience working with open source projects.
* Experience with machine learning algorithms is a plus but not strictly required.
* Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes)
* Have a good sense of teamwork. Strong communication skill.
The Shanghai AI Lab pioneered the research and development of the Deep Graph Library (https://www.dgl.ai/), the world's leading platform that combines deep learning and structural data representation of graphs. This emerging new field holds promises to impact a wide surface of applications, both old and new, and is a fertile ground to push the frontier of AI research.
As an MLE, you will join a vibrant team with a diverse set of expertise in both machine learning and system research, and work with other world-class scientists and engineers in AWS Machine Learning group around the globe and across the boarders. Collaborate with data scientists, software engineers, and product managers to integrate ML solutions into production systems. Optimize existing ML models for improved performance, scalability, and efficiency. Mentor junior team members and contribute to the growth of the ML team.
With a particular emphasis on open-source projects, your work directly impacts researchers around the world. You should be comfortable diving deep into technical architectures and requirements, be able to quickly identify solutions to challenges discovered during development. A commitment to team work, hustle, and strong communication skills (to both business and technical partners) are absolute requirements.
BASIC QUALIFICATIONS
* M.S. degree in Computer Science and Engineering or related disciplines.
* 3+ years of software development experience in system infrastructure including but not limited to high performance computing, distributed system, compiler stack, GPU programming.
* Strong proficiency in Python and experience with ML frameworks such as PyTorch.
* Experience with deep learning and neural network architectures
* Proficiency in one of the following languages: C++ or Python.
PREFERRED QUALIFICATIONS
* Experience working with open source projects.
* Experience with machine learning algorithms is a plus but not strictly required.
* Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes)
* Have a good sense of teamwork. Strong communication skill.