Researcher Graduate

About the Team: The Applied Machine Learning Enterprise team combines system engineering and machine learning to develop and operate big model service platform that offers businesses Model-as-a-Service solutions (MaaS) to both the big model vendors and users. We are actively seeking talented Researchers specializing in Large Language Models (LLM) to join our team. Responsibilities: In this role, you will be at the forefront of cutting-edge research and development of advanced techniques for MaaS solutions including model continuous pretraining, fine-tuning, evaluation, inference capabilities and also LLM application/agent development. Your primary responsibility will be to: - Lead the creation of next-generation, high-capacity LLM platforms and innovative products. - Work closely with cross-functional teams to plan and implement projects harnessing LLMs for diverse purposes and vertical domains. - Maintain a deep passion for contributing to the success of large models is essential in this innovative and fast-paced team environment. Minimum Qualifications: - Ph.D. in Computer Science, Artificial Intelligence, or a related field. - Have prior experience working with training and inference of large language models. - Strong understanding of cutting-edge LLM research (e.g., long context, multi modality, alignment research etc.) and possess practical expertise in effectively implementing these advanced systems. - Proficiency in programming languages such as Python or C++ and a track record of working with deep learning frameworks (e.g., pytorch, deepspeed, etc.). - Strong understanding of distributed computing framework & performance tuning and verification for training/finetuning/inference. Being familiar with PEFT or MoE is a plus. Preferred Qualifications: - Excellent problem-solving skills and a creative mindset to address complex AI challenges. Demonstrated ability to drive research projects from idea to implementation, producing tangible outcomes. - Published research papers or contributions to the LLM community would be a significant plus. - Experience with inference tuning and Inference acceleration. Have a deep understanding of GPU and/or other AI accelerators, experience with large scale AI networks, pytorch 2.0 and similar technologies. - Experience with large scale machine learning systems' scheduling and orchestration, familiar with Kubernetes and Cloud Native technologies. - Experience with deploying AI models into production environments, testing and evaluation of AI systems, LLM application & agent development is desirable.