Research Scientist: AI Data Flywheel & LLM Platform

Bytedance; San Jose (CA); Sur place; USD 218 000 - 480 000; Plein temps

Résumé du poste

ByteDance's US team in California leads research and development for Large Language Models and AI agent systems, delivering MaaS solutions to enterprise clients. The role focuses on building a scalable big-model-as-a-service platform, covering offline training, fine-tuning, online inference, and efficient GPU resource management.

The successful candidate will contribute to model alignment, evaluation, and data-driven feedback loops, collaborating with engineers and researchers to push end-to-end

Qualifications

• Currently pursuing or recently completed a Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related technical field.
• Research experience in one or more of the following areas: LLM post-training and alignment, model evaluation, test-time scaling, agent systems, or large-scale data curation and optimization.
• Demonstrated research ability through publications, substantial research projects, or internships.
• Ability to work independently on open-ended research problems, from problem formulation to experimental execution.

Responsabilités

• Building a next-generation big model as a service platform to serve hundreds of LLMs based applications.
• To develop and maintain the big model as a service platform, including offline training/finetuning, online inference, model management, and resource orchestration, etc.
• To manage a huge number of GPU resources and provide computing power efficiently.

Connaissances

Ph.D. in CS/ML/AI

LLM research

Publications/Projects

Independent problem solving

Formation

Ph.D. in Computer Science or related

Description du poste

ByteDance's US team in California leads research and development for Large Language Models and AI agent systems, delivering MaaS solutions to enterprise clients. The role focuses on building a scalable big-model-as-a-service platform, covering offline training, fine-tuning, online inference, and efficient GPU resource management.

The successful candidate will contribute to model alignment, evaluation, and data-driven feedback loops, collaborating with engineers and researchers to push end-to-end