The Applied Machine Learning Enterprise AI Foundation US team is looking to solve the problem of designing, developing, and operating MaaS solutions in the US and rest of the world overseas of mainland China, by building full stack end-to-end solutions covering text and multi-modality LLM algorithms, prompting engineering, LLM model alignment, and intelligent agents, etc.
Requirements
- Strong understanding of cutting-edge LLM research (e.g., long context, multi modality, alignment research, agent ecosystem, etc.)
- Practical expertise in effectively implementing these advanced systems
- Proficiency in programming languages such as Python, Rust, or C++
- Track record of working with deep learning frameworks (e.g., pytorch, deepspeed, megatron, vllm, etc.)
- Strong understanding of distributed computing framework & performance tuning and verification for training/finetuning/inference
- Familiarity with PEFT, RL, MoE, CoT or Langchain is a plus
- Experience with inference tuning and Inference acceleration
Responsibilities
- 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
Other
- Ph.D./Master in Computer Science, Data Science, Artificial Intelligence, or a related field
- 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 large scale machine learning systems' scheduling and orchestration
