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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:** Amazon.com, Inc. - **Location:** Seattle, WA, United States - **Experience:** Starter - **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 7, 2026 - **Apply:** https://www.careerjet.com/job/us49df53c1ead7bf9f03cc73f2a51fb4f9/eaa ## 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. Preferred Qualifications: - 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. ## Description 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) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Phel, a native Lisp for PHP](https://www.wearedevelopers.com/videos/791-phel-a-native-lisp-for-php) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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