Student Researcher; Seed - LLM - Model PhD

Student Researcher (Seed - LLM - Model) - 2026 Start (PhD)

Location:

San Jose

Team:

Technology

Employment Type:

Intern

Job Code:

A45914

Team Introduction The Seed-LLM-Model team is dedicated to foundational algorithm research for LLM models, focusing on model architecture, optimization, and stability. This ensures the performance and efficiency of large model training and inference, providing a foundation for downstream tasks. We are looking for talented individuals to join us for an internship in 2026. PhD internships at Byte Dance provide students with the opportunity to actively contribute to our products and research, and to the organization’s future plans and emerging technologies.

Our dynamic internship experience blends hands‑on learning, enriching community‑building and development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis – we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date). Summer Start Dates – May 11th, 2026 – May 18th, 2026 – May 26th, 2026 – June 8th, 2026 – June 22nd, 2026.

• Participate in the research and development of cutting‑edge algorithms with the possibility to publish top international papers and apply for patents.
• Conduct in‑depth research for cutting‑edge technologies in the fields of large language models/Multi Modal Machine Learning, and have opportunities for applying them to solve practical problems in the industry.

• Currently pursuing a PhD in artificial intelligence, computer science, automation, mathematics, or a related technical discipline.
• Solid foundation in data structure and algorithm design, proficient in Python/C++, proficient in deep learning frameworks such as PyTorch and Tensor Flow, proficient in distributed large language model training framework such as Megatron/FSDP/Deepspeed.
• Good reading and writing skills and a solid foundation in mathematics.
• Strong sense of responsibility, proactive, with good communication and teamwork skills.
• Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.
• Must be able to commit to a 12‑week full‑time work period during Summer or Fall 2026.

• Pre‑trained basic technologies, including efficient training and encapsulated deployment services, NLP, CV, video, Multi Modal Machine Learning and other related pre‑trained models and their downstream applications are preferred.
• Candidates who have published papers in top academic conferences, and have achieved excellent results in competitions in the fields of Multi Modal Machine Learning, Computer Vision, or Machine Learning are preferred.

Job Information The hourly rate range for this position in the selected city is $60‑ $60.

Benefits may vary depending on the nature of employment and the country work location. Interns have day one access to health insurance, life insurance, wellbeing benefits and more. Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year). Interns who are not working 100% remote may also be eligible for housing allowance.

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…