AI Infra Engineer (Machine Learning)
Supports the research and investment teams by working on model inference and AI infrastructure. This role does not focus on model training.
- Candidates from C9 universities or equivalent top-tier institutions are preferred.
- Compensation is flexible for senior candidates.
Responsibilities
- Responsible for the architecture design, key technology research, and implementation of machine learning frameworks.
- Optimize machine learning frameworks based on business scenarios to improve stability, usability, and model training efficiency.
- Promote the integration of machine learning frameworks and AI platforms, including training scheduling mechanisms, automated training frameworks, model evaluation systems, and inference optimization.
Requirements
- Bachelor's degree or above in Computer Science, Electronics, Automation, or related disciplines.
- Junior or senior, strong proficiency in C++ and Python.
- Solid foundation in deep learning algorithms, strong understanding of common machine learning algorithms, and capability for algorithm research and innovation.
- Deep understanding of the underlying architecture and mechanisms of at least one mainstream deep learning framework, such as PyTorch, TensorFlow, MXNet, or PaddlePaddle.
- Strong curiosity about cutting-edge technologies and the ability to independently drive technical exploration and implementation.
Preferred Qualifications
- Publications in top-tier AI/ML conferences or journals, including but not limited to NeurIPS, ICML, CVPR, and COLT.
- Familiarity with Kubernetes clusters and InfiniBand/RDMA technologies.
- Understanding of MLOps technologies, with hands-on experience applying algorithms and machine learning frameworks in production environments.
FAQs
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