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# Machine Learning Engineer Graduate (E-Commerce Recommendation Foundation) - 2027 Start

- **Company:** Tiktok Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Starter
- **Salary:** $121,600.0 - $243,200.0
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning, Natural Language Processing, Recommender Systems, Pytorch, Large Language Models, Deep Learning, Information Technology
- **Published:** August 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=804e7cfda9d8a2e3

## About the Role

* Individuals who are completing or have recently completed a Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline.
 * Solid foundation in machine learning and deep learning, with strong interest in LLMs and generative recommendation.
 * Proficiency in Python and experience with deep learning frameworks such as PyTorch.
 * Self-driven, with a strong research mindset and solid engineering skills.

Preferred Qualifications:

 * Experience with pre-training, mid-training, or post-training of LLMs or Foundation Models.
 * Research or project experience in generative recommendation, LLM-native recommendation, or multimodal semantic tokenization.
 * Publications on LLM-related topics at top-tier machine learning or natural language processing conferences, such as NeurIPS, ICML, ICLR, ACL, EMNLP, or NAACL, or strong achievements in major technical competitions., 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

## Description

1. Participate in the full training lifecycle of Recommendation Foundation Models, including pre-training, mid-training, and post-training.
 2. Design and train multimodal semantic tokenizers for recommendation items, leveraging multimodal foundation models to encode rich item content into discrete semantic tokens and raise the performance ceiling of Recommendation Foundation Models.
 3. Develop LLM-native recommendation by incorporating recommendation tasks directly into large language model training and leveraging world knowledge to improve recommendation quality.
 4. Build the next generation of recommendation systems powered by Recommendation Foundation Models, spanning retrieval, ranking, and end-to-end generative recommendation.


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