Research Scientist: AI Foundation Models & ML Systems

ByteDance; San Jose (CA); On-site; USD 254,000 - 480,000; Full time

Job summary

ByteDance’s Seed team in the United States leads research on large-scale model training, reinforcement learning frameworks, and AI foundation models. We seek a PhD-level engineer to build scalable ML infrastructure across distributed clusters and heterogeneous hardware.

You will advance training and inference pipelines, contribute to model lifecycle tooling, and collaborate with researchers and engineers to push the frontier of foundation models.

Qualifications

• PhD in CS/EE/related field completed or near completion.
• Strong coding skills in C/C++ and Python.
• Experience with distributed training infrastructure.
• Familiarity with deep learning frameworks and optimization.

Responsibilities

• Design and build scalable infrastructure for large-scale model training, evaluation, and inference.
• Optimize distributed training systems across compute, memory, and communication.
• Improve system reliability, efficiency, and observability for large-scale workloads.
• Develop frameworks for evaluation, data processing, and model lifecycle management.
• Co-design systems and algorithms to improve performance of foundation models.

Skills

C/C++

Python

Distributed systems

Deep learning frameworks

Education

PhD in CS/EE/related field

Job description

ByteDance’s Seed team in the United States leads research on large-scale model training, reinforcement learning frameworks, and AI foundation models. We seek a PhD-level engineer to build scalable ML infrastructure across distributed clusters and heterogeneous hardware.

You will advance training and inference pipelines, contribute to model lifecycle tooling, and collaborate with researchers and engineers to push the frontier of foundation models.