Research Engineer Graduate (AI Training Systems Reliability & Performance - Seed Infra) - 2026 Start (PhD)

ByteDance

$241K — $456K *
Enterprise Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • PhD in Computer Science, Engineering, Physics, Mathematics, or related field (or recent graduate)
  • Proficiency in programming with C++ and Python
  • Understanding of PyTorch workflows and distributed systems
  • Familiarity with CUDA and NCCL for GPU operations
  • Experience with performance profiling tools (e.g., torch.profiler, Nsight)

Responsibilities

  • Enhance reliability and performance of large-scale ML training systems
  • Develop observability and debugging tools for distributed ML workloads
  • Identify and optimize performance issues in GPU, networking, and storage
  • Contribute to multi-GPU and multi-node training frameworks
  • Work with teams to boost system scalability and efficiency
  • Assist with incident analysis and ensure operational stability

Benefits

  • Medical, dental, and vision insurance from day one
  • 401(k) savings plan with company match
  • Paid parental leave and disability coverage
  • Life insurance and wellbeing benefits
  • 10 paid holidays, 10 sick days, and 17 days of personal time off per year
Full Job Description
Responsibilitie

About the Team The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Responsibilities - Improve the reliability and performance of large-scale training systems across pre-training, fine-tuning, evaluation, and inference - Build observability, profiling, and debugging tools for distributed ML workloads - Identify and optimize performance bottlenecks across GPU, networking, and storage layers - Contribute to distributed training frameworks in multi-GPU and multi-node environments - Collaborate with model and infrastructure teams to improve system scalability and efficiency - Support incident analysis and operational stability

Qualification

Minimum Qualifications - Individuals who are completing or have recently completed a PhD degree in Computer Science, Electrical Engineering, Electrical and Computer Engineering, Physics, Mathematics, or a related discipline. - Strong programming skills in C++ and Python - Solid understanding of PyTorch training workflows and distributed runtime behavior - Familiarity with CUDA execution, NCCL communication, and GPU systems fundamentals Preferred Qualifications - Experience with performance profiling and debugging tools (e.g., torch.profiler, Nsight) - Familiarity with distributed training or parallelization strategies (e.g., FSDP, Megatron-LM) - Ability to analyze and optimize performance in complex ML training system

Job Information

【For Pay Transparency】Compensation Description (Annually)

The base salary range for this position in the selected city is $241680 - $456000 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.

About Doubao (Seed)

Established in 2023, the ByteDance Seed team is dedicated to pioneering new paths toward artificial general intelligence. We aspire to advance the frontier of intelligence to drive progress for both technology and society.

With a long-term vision for the AI sector, the Seed team's research spans MLLM, GenMedia, AI for Science, and Robotics. We maintain a global presence with laboratories and career opportunities across China, Singapore, and the United States. To date, we have launched industry-leading general foundation models and cutting-edge multimodal capabilities. Our technology powers over 50 application scenarios - including Doubao, Jimeng, TRAE, Dola and Dreamnia - and serves enterprise customers through Volcano Engine and BytePlus. Third-party data shows that the Doubao App ranks first in user volume in the Chinese market, while Doubao foundation models lead the industry in average daily token consumption.

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