Research Engineer - Multimodal Training Infrastructure (Seed Infra)

ByteDance

$254K — $480K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Expertise in large-scale distributed training of LLMs and multimodal models
  • Strong background in systems research for optimizing ML systems
  • Experience with parallelism strategies on GPU clusters
  • Programming skills for implementing production-grade ML systems
  • Understanding of algorithm-system co-design and optimization for training efficiency

Responsibilities

  • Conduct research on large-scale infrastructure for efficient AI model training
  • Design and optimize distributed training strategies for multimodal models
  • Investigate reliability techniques for long-term training workloads
  • Optimize network and GPU memory management for training performance
  • Analyze and resolve performance bottlenecks in training systems
  • Translate research into scalable infrastructure solutions

Benefits

  • Day one access to medical, dental, and vision insurance
  • 401(k) savings plan with company match
  • Paid parental leave and short/long-term disability coverage
  • Life insurance and wellbeing benefits
  • 10 paid holidays and 10 paid sick days per year
  • 17 days of Paid Personal Time with increasing accruals by tenure
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. Responsibilities - Conduct research and development on large-scale infrastructure to enable efficient training of foundation models, multimodal LLMs, and image/video generation models - Design and optimize distributed training strategies for multimodal LLMs, including parallelism schemes, computation and communication optimization, and throughput scaling on large GPU clusters - Investigate system reliability and resilience techniques, such as fast checkpointing, fault tolerance, and failure diagnosis for long-running training workloads - Research and optimize network, scheduling, and GPU memory management across the training stack, driving cross-layer performance improvements - Analyze performance bottlenecks in exascale training systems and propose principled, data-driven optimization methods - Bridge cutting-edge research and large-scale production deployment by translating research ideas into scalable, real-world infrastructure solution

Qualification

Minimum Qualifications - Deep expertise in large-scale distributed training of LLMs and multimodal models - Strong systems research background with demonstrated ability to design, build, and optimize large-scale ML systems - Proven experience with parallelism strategies (e.g., data, model, pipeline, expert parallelism) and performance optimization on large GPU clusters - Strong programming skills and hands-on experience implementing production-grade ML systems or infrastructure - Solid understanding of algorithm-system co-design and cross-layer optimization for training efficiency, scalability, and reliability

Job Information

【For Pay Transparency】Compensation Description (Annually)

The base salary range for this position in the selected city is $254400 - $480000 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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