Research Engineer - LLM Training Infrastructure - Seed Infra Seattle Regular

ByteDance; Seattle (WA); On-site; USD 232,560 - 427,500; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) savings plan with company match

Paid parental leave

Short-term and long-term disability coverage

Life insurance

Job summary

ByteDance is seeking a skilled professional for the Seed Infrastructures team in Seattle, focusing on research and development for large-scale LLM training infrastructure. The candidate should be proficient in designing and optimizing distributed training strategies and analyzing performance bottlenecks in systems.

The ideal applicant will have strong programming skills in Python and/or C++, along with experience in ML systems and training infrastructure development. The position offers a competitive salary and a comprehensive benefits package.

Qualifications

• Experience with large-scale distributed training for LLMs.
• Strong programming skills in Python and/or C++.
• Solid understanding of training stack internals (PyTorch, CUDA, NCCL).

Responsibilities

• Conduct research and development on large-scale LLM training infrastructure.
• Design and optimize distributed training strategies for LLMs.
• Analyze performance bottlenecks in exascale training systems.

Skills

Experience with large-scale distributed training for LLMs

Strong programming skills in Python and/or C++

Strong background in ML systems / training infrastructure development

Proficiency in parallelism strategies (DDP, FSDP, model/pipeline/expert parallelism)

Solid understanding of training stack internals (PyTorch, CUDA, NCCL)

Experience in performance optimization (memory, communication, throughput)

Job description

Location: Seattle

Team: Technology

Employment Type: Regular

Job Code: A78978

Responsibilities

The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high‑performance inference, and heterogeneous hardware compilation technologies for AI foundation models.

• Conduct research and development on large‑scale LLM training infrastructure and efficiency
• Design and optimize distributed training strategies for 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 AI infrastructure solutions

Qualifications

Minimum Qualifications

• Experience with large‑scale distributed training for LLMs
• Strong programming skills in Python and/or C++
• Strong background in ML systems / training infrastructure development
• Proficiency in parallelism strategies (DDP, FSDP, model/pipeline/expert parallelism)
• Solid understanding of training stack internals (PyTorch, CUDA, NCCL)
• Experience in performance optimization (memory, communication, throughput)

Preferred Qualifications

• Hands‑on experience with distributed training frameworks and large‑scale LLM infrastructure
• Experience leading or mentoring engineering teams or cross‑functional projects
• Publications in top‑tier AI, systems, or HPC conferences (ICML, OSDI, SOSP, NSDI, SIGCOMM, MLSys) or strong open‑source contributions
• Familiarity with benchmarking AI accelerators or large‑scale LLM evaluation (e.g., ByteMLPerf)

Job Information

The base salary range for this position in the selected city is $232,560 - $427,500 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.

Reasonable Accommodation

ByteDance is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request