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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer Graduate (E-Commerce Recommendation Live) - 2027 Start - **Company:** Tiktok Inc. - **Location:** San Jose, CA, United States - **Experience:** Starter - **Salary:** $128,000.0 - $316,800.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, C++ (Programming Language), Computer Programming, Data Structures, Python (Programming Language), Machine Learning, Recommender Systems, Reinforcement Learning, Pytorch, Transfer Learning, Large Language Models, Deep Learning, Information Technology, Machine Learning Operations - **Published:** August 5, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=49ef8909cb6c08ce ## 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 at least one of the following areas: recommendation systems, search, advertising, NLP, multimodal learning, or large-scale applied AI. * Strong programming skills in Python or C++, and hands-on experience with deep learning frameworks such as PyTorch. * Good understanding of data structures, algorithms, and large-scale model training or production machine learning systems. * Strong analytical and problem-solving skills, with the ability to translate business problems into effective modeling solutions. * Self-driven and results-oriented, with the ability to take ownership of model iteration and online impact from end to end., * Experience in recommendation systems, especially in live commerce, e-commerce, search, ads, or other large-scale consumer products. * Experience with generative recommendation, large recommendation models, retrieval and ranking systems, or related recommendation architecture upgrades. * Experience with LLMs or multimodal foundation models, including pre-training, post-training, representation learning, contrastive learning, SFT, or RL-based optimization. * Experience in cross-domain transfer learning, LTV modeling, long-term value optimization, causal inference, or debiasing. * Experience with long-sequence user behavior modeling, multi-task learning, multi-interest modeling, or large-scale distributed training and inference optimization. * Publications in top-tier conferences such as NeurIPS, ICML, ICLR, KDD, ACL, CVPR, SIGIR, or RecSys, or strong achievements in major technical competitions. * Strong curiosity about new technologies, fast learning ability, and a passion for solving challenging real-world problems., 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 The Global E-commerce Recommendation Live Algorithm team is responsible for the core recommendation stack for live commerce, covering the full pipeline from recall and pre-ranking to ranking and mixed ranking. The team operates in a highly dynamic environment where live room status changes in real time, conversion signals are sparse, and user intent must be understood across content, commerce, and transaction scenarios., * Build and optimize recommendation models across recall, pre-ranking, ranking, and mixed ranking to improve GMV, conversion, watch time, and long-term user value. * Develop cross-domain and multimodal modeling solutions that connect videos, live streams, products, and user behavior to better power live commerce recommendations. * Advance next-generation recommendation technologies, including generative recommendation, large recommendation models, reinforcement learning, and long-term value optimization. * Partner with cross-functional teams to launch scalable solutions, run experiments, and turn research into measurable business impact. ## Related Videos - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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