LLM Research Scientist Graduate (eCommerce Recommendation)- 2025 Start (MS)

al progress.
Therefore, we will continuously invest more resources in the research of recommendation algorithms based on large language models.

We are looking for talented individuals to join our team in 2025. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.

Responsibilities:

1. Continuously track and understand the latest research progress of large language models, including pre-training, fine-tuning, instruction fine-tuning, prompt engineering, multimodal large models, etc.
2. Actively explore the combined application of large language models and recommendation systems to solve practical problems in recommendation systems, including person-goods matching, commodity governance, interest exploration, commodity understanding, and generative recommendation.
3. Cooperate with the infrastructure team to improve the efficiency of large model training, develop large model services, and improve the stability and scalability of training and services.
4. Cooperate with scientists in the team to publish research results in top academic conferences and expand the influence of the team.

Qualifications

Minimum Qualifications:

• Final year or recent graduates with a background in Computer Science, related technical field or equivalent industry experience, with top artificial intelligence conference papers (NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, etc.) in machine learning (ML), computer vision (CV), natural language processing (NLP) and other fields.
• Hands-on experience in one or more of the following areas: Large Language Models (LLM), Machine Learning, Deep Learning, Recommender Systems, Data Mining, or Natural Language Processing
• Excellent programming skills, data structure and algorithm skills, proficient in C/C++ or Python programming language, candidates with awards in ACM/ICPC, NOI/IOI, Top Coder, Kaggle and other competitions are preferred.
• Familiar with architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/PyTorch/MXNet)

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $113500 - $168150 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.