Responsibilities

• Engage in the development and optimization of large-scale pre-training language models, including model architecture design, parallel training strategies, and performance improvements
• Drive research and implementation of advanced LLM post-training techniques, including chain-of-thought tuning, preference alignment, and RL for reasoning
• Develop and optimize data collection pipelines for model training, including data de-duplication, cleaning, and verification
• Design and implement solutions for model deployment, including inference optimization and scaling strategies
• Collaborate with cross-functional teams to apply LLM capabilities in various business scenarios, such as materials science
• Stay current with the latest developments in the field and contribute to the company's technical roadmap
  

• Master's or Ph.D. in Computer Science, AI, or related field
• 5+ years of experience in machine learning, with specific focus on NLP and LLMs
• Strong understanding of transformer architectures and modern LLM frameworks(BERT, GPT, T5)
• Extensive experience with deep learning frameworks (PyTorch, TensorFlow, JAX)
• Strong programming skills in Python and proficiency with ML tools (Hugging Face, DeepSpeed)
• Proven track record in training and optimizing large-scale language models (10B+ parameters) is preferred
• Experience with distributed training systems (Megatron) and optimization techniques is preferred