AI Infrastructure Engineer

We are currently working with a global quantitative asset manager in seeking an lead engineer for their AI infrastructure team.

Responsibilities:

• Lead the architecture design: Take charge of designing the architecture for deep learning algorithm frameworks, ensuring they are robust, scalable, and efficient. This involves understanding the requirements of various business scenarios and tailoring the architecture to meet these needs.
• Conduct key technology research: Engage in cutting-edge research to identify and develop key technologies that can enhance the performance and capabilities of deep learning frameworks. This includes staying updated with the latest advancements in the field and integrating them into the framework.
• Implement deep learning algorithm frameworks**: Oversee the implementation of deep learning algorithm frameworks, ensuring they are optimized for performance and usability. This involves writing efficient code, debugging, and testing to ensure the frameworks meet the desired specifications.
• Optimize the framework: Continuously improve the framework based on feedback and performance metrics. This includes enhancing its stability, usability, and efficiency, particularly in the context of model training. The goal is to make the framework more user-friendly and effective in various business scenarios.
• Integrate with AI platforms: Work on integrating the deep learning framework with existing AI platforms. This involves developing advanced training scheduling mechanisms to ensure efficient use of resources, creating automated training systems to streamline the training process, and establishing robust model evaluation processes to assess the performance of trained models.
• Inference optimization: Focus on optimizing the inference process to ensure that models can make predictions quickly and accurately. This involves fine-tuning the framework to reduce latency and improve the overall performance of the inference process.

Qualifications:

• Educational background: A Bachelor's degree or higher in Computer Science, Electronics, Automation, or a related field is required. This educational foundation provides the necessary theoretical knowledge and practical skills to excel in this role.
• Deep learning algorithms: A strong foundation in deep learning algorithms is essential. Candidates should be proficient in common deep learning and machine learning algorithms and capable of conducting innovative algorithm research. This involves understanding the mathematical principles behind these algorithms and being able to apply them to solve complex problems.
• Deep learning frameworks: Expertise in the underlying architecture and mechanisms of at least one deep learning framework (such as PyTorch, TensorFlow, MxNet, Paddle) is required. Proficiency in PyTorch is preferred due to its widespread use and flexibility. This involves understanding how these frameworks work at a low level and being able to optimize them for specific tasks.
• Programming skills: Excellent programming skills are a must. Candidates should be proficient in C++ and Python, as these languages are commonly used in the development of deep learning frameworks. This involves writing clean, efficient, and maintainable code.
• Passion for technology: A genuine enthusiasm for cutting-edge technology is crucial. Candidates should have the ability to independently drive the exploration and implementation of new technologies. This involves staying updated with the latest trends and advancements in the field and being able to apply them to improve the framework.