Drimva

ByteDance

Research Scientist - Model Capability Boundary Exploration and AI Data Flywheel System Development - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

San Jose, California · Full-time · Posted Oct 28, 2026

Education
Currently pursuing or recently completed a Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related technical field.

About the job

Develops a real-world AI data flywheel to turn platform logs, cases, feedback, and environmental information into structured insights for improving foundation models and business-system agents.

What you'll do

  • Build a model-as-a-service platform for LLM-based applications.
  • Develop and maintain platform capabilities for training, fine-tuning, inference, model management, and resource orchestration.
  • Manage GPU resources and provide computing capacity efficiently.

What they ask for

  • 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 at least one of: 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 through experimental execution.

Nice to have

  • Interest in foundation models and data-centric AI, including data flywheels, continual learning, data curation and valuation, or algorithm-infrastructure co-design.
  • Multiple first-author publications, or relevant internship/research experience, especially with scalable ML systems and real-world deployment.

Tools

LLM, GPU

AI focus

Build an AI data flywheel from real-world usage data and feedback to improve foundation models and agent systems, including their tools, memory, workflows, and environment.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 6, 2026 (UTC)