Develop and optimize large-scale pre-training language models, implement advanced LLM techniques, and collaborate with teams for deployment solutions.
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.
Qualifications
- 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.
Top Skills
Bert
Deepspeed
Gpt
Hugging Face
Jax
Megatron
Python
PyTorch
T5
TensorFlow
Patsnap Singapore Office
47 Scotts Road - Goldbell Towers, #11-03, Singapore, 228233
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