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Company Overview
This company is a leading provider of innovation intelligence, empowering organizations to make smarter decisions and accelerate their innovation journey. Their platform combines patent data, scientific literature, and other innovation resources with powerful analytics and visualization tools. They work with global corporations, research institutions, and legal firms to unlock insights, identify opportunities, and stay ahead of the competition.
A successful candidate will engage in the development and optimization of large-scale pre-training language models, including model architecture design, parallel training strategies, and performance improvements. This role involves driving research and implementation of advanced LLM post-training techniques, including chain-of-thought tuning, preference alignment, and RL for reasoning. The individual will develop and optimize data collection pipelines for model training, including data de-duplication, cleaning, and verification. Designing and implementing solutions for model deployment, including inference optimization and scaling strategies, will also be a key responsibility. Collaboration with cross-functional teams to apply LLM capabilities in various business scenarios, such as materials science, is expected. Staying current with the latest developments in the field and contributing to the company’s technical roadmap are also important aspects of this role.
Qualifications
Candidates must possess a Master’s or Ph.D. in Computer Science, AI, or a related field, along with 5+ years of experience in machine learning, with a specific focus on NLP and LLMs. A strong understanding of transformer architectures and modern LLM frameworks (BERT, GPT, T5) is required. Extensive experience with deep learning frameworks (PyTorch, TensorFlow, JAX) and strong programming skills in Python, coupled with proficiency in ML tools (Hugging Face, DeepSpeed), are essential. A proven track record in training and optimizing large-scale language models (10B+ parameters) is preferred, as is experience with distributed training systems (Megatron) and optimization techniques.
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