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# Machine Learning Engineer, E-commerce Recommendation Foundation - USDS

- **Company:** Tiktok Inc.
- **Location:** Seattle, WA, United States
- **Salary:** $129,960.0 - $246,240.0
- **Contract:** Permanent contract
- **Skills:** Program Optimization, Information Retrieval, Python (Programming Language), Machine Learning, Recommender Systems, UML, Reinforcement Learning, Pytorch, Large Language Models, Deep Learning, Software Library
- **Published:** June 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3c0ce0fc14b5d3b7

## About the Role

Do you have experience in Machine learning libraries?, Minimum Qualifications - Solid theoretical foundation in machine learning, deep learning, or information retrieval. - Proficiency in Python and familiarity with mainstream deep learning frameworks (e.g., PyTorch). - Strong passion for intelligent recommendation systems and a self-driven research mindset. Preferred Qualifications - Experience in large-scale recommendation system development or large-model training, with notable technical achievements in a sub-area. - Research experience or publications in LLMs, multimodal learning, reinforcement learning, or generative recommendation. - Familiarity with pre-training and post-training processes for large language models (LLMs) or Foundation Models.

## Description

The E-commerce Recommendation Foundation team is dedicated to building the next-generation recommendation intelligence. We aim to develop a unified Foundation Model that supports multi-business and multi-scenario recommendation systems, covering the full pipeline from retrieval and ranking to re-ranking, and driving a comprehensive upgrade in intelligence and generative capability. We believe the future of recommendation systems goes beyond predicting click-through rates - it lies in understanding the relationship between people and content, and in generating new connections. The team is exploring an event-sequence-driven generative recommendation paradigm, deeply integrating large language models (LLMs), multimodal understanding, reinforcement learning, and system optimization to advance recommendation systems toward general-purpose intelligent agents. We value original exploration and encourage both research thinking and engineering excellence. Every team member is empowered to propose
hypotheses and validate ideas in an open environment - your code and papers may help define the next paradigm of recommendation systems. We seek individuals with a general intelligence mindset to join us in redefining the future of recommendation. - Build and optimize cross-scenario shared Foundation Models to enable unified modeling and efficient inference. Advance the event-sequence-driven generative recommendation paradigm, integrating multimodal understanding and generative capabilities. - Apply LLM technologies across retrieval, ranking, and re-ranking stages; participate in model training, inference optimization, and system co-design. - Explore the integration of LLMs / VLMs with recommendation systems to develop adaptive and evolving intelligent recommenders. - Research end-to-end generative recommendation and system optimization methods that balance efficiency and user experience.


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