Machine Learning Engineer (AI infra) in London

Base location is Shanghai, both full-time and internship positions are available. We welcome outstanding Chinese talent from around the world.

About Yanfu: Shanghai Yanfu Investment Management Co., Ltd. was established in 2019 and is a technology company engaged in investment management using quantitative methods. The strategy team members have diverse backgrounds: experts with years of experience in leading hedge funds overseas, academic experts who joined the industry after teaching at universities in the United States, and core members who have grown within Yanfu after graduating from top domestic and international universities. The core members of the engineering team come from top institutions such as Tsinghua, Peking University, and Jiaotong University, with most having work experience in leading internet companies. The team has rich technical experience and a good technical atmosphere. The company aims to build Yanfu into a widely recognised leading asset management brand over the next 10-20 years. Yanfu encourages open communication and collaboration, believing that a free and open culture is the soil for outstanding talent to unleash their creativity. We hope every employee can fully realise their career development potential in a friendly collaborative environment.

Job Responsibilities:

• Responsible for the research, optimisation, and implementation of machine learning/deep learning models to enhance trading signal performance.
• Research cutting-edge algorithms and optimisation techniques to drive technological iteration and business innovation.

Qualifications:

• Bachelor's degree or above in a computer-related field from a well-known domestic or international university.
• Solid foundation in algorithms and mathematics, familiar with commonly used machine learning/deep learning algorithms (XGBoost/LSTM/Transformer, etc.).
• Proficient in Python/C++, familiar with frameworks such as PyTorch/TensorFlow.
• Excellent business understanding and independent problem-solving skills, good teamwork awareness, and communication skills.

Preferred Qualifications:

• Familiar with CUDA, understanding mainstream parallel programming and performance optimisation techniques.
• Experience in practical model engineering optimisation (e.g., training or inference acceleration).
• Familiar with parallel training frameworks such as DeepSpeed and Megatron.
• Familiar with Triton and Cutlass, able to write efficient operators based on business needs.
• Familiar with multimodal learning, large-scale pre-training, and modality alignment technologies.