Graduate Research Scientist, AI Systems & Infrastructure

ByteDance; San Jose (CA); On-site; USD 218,000 - 388,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with company match

Paid parental leave

Short-term and long-term disability

Life insurance

Wellbeing benefits

10 holidays per year

10 sick days per year

17 days of Paid Personal Time

Job summary

ByteDance Seed team is hiring graduates to design and build scalable infrastructure for large-scale model training, evaluation, and inference in the United States. You will optimize distributed systems, improve reliability, and contribute to the lifecycle management of models across heterogeneous hardware.

Ideal candidates have completed a BS in a technical field, strong coding in C/C++ or Python, and some experience with distributed or ML systems through internships or projects.

Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering, Electrical and Computer Engineering, Physics, Mathematics, or related discipline.
• Excellent coding ability in C/C++ or Python; strong data structures and algorithms.
• Interest or project experience in relevant areas; collaboration skills.

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

Bachelor's degree in CS/EE

C/C++

Python

Distributed systems

ML systems

Education

Bachelor's degree in relevant field

Job description

ByteDance Seed team is hiring graduates to design and build scalable infrastructure for large-scale model training, evaluation, and inference in the United States. You will optimize distributed systems, improve reliability, and contribute to the lifecycle management of models across heterogeneous hardware.

Ideal candidates have completed a BS in a technical field, strong coding in C/C++ or Python, and some experience with distributed or ML systems through internships or projects.