Machine Learning Engineer Graduate (E-Commerce Recommendation Mall) - 2027 Start
Actively Hiring
Full-time $121k - $243k Posted 18 days ago
Responsibilities
- check_circle Generative Recommendation Research: Drive the evolution of recommender systems from discriminative to generative paradigms; explore frontier directions such as generative retrieval and generative re-ranking/blending; continuously improve personalization capabilities while deeply optimizing the training and inference efficiency of generative models on GPUs.
- check_circle LLM for Recommendation Research: Leverage Large Language Models (LLMs), Reinforcement Learning (RL), and related techniques to enhance the semantic understanding and reasoning capabilities of recommender systems, addressing core business challenges in e-commerce scenarios (e.g., cold start, long-tail item distribution, and user intent understanding).
- check_circle Agentic Recommendation Research: Explore the construction of self-evolving agents and leverage agents to continuously optimize recommender systems; drive the evolution of recommender systems toward agentic architectures capable of keenly perceiving user context and making real-time, personalized decisions and adjustments.
- check_circle Long-Term Value and User Experience Modeling: Explore replacing traditional heuristic rule-based systems with LLM and agent capabilities; build next-generation algorithms for measuring and optimizing long-term value (LTV) and user experience, enabling sustainable growth of the platform ecosystem.
Basic qualifications
- 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 research or project experience in at least one of the following areas: large language models, reinforcement learning, generative models, recommender systems or information retrieval.
- Proficient in Python and at least one mainstream deep learning framework (e.g., PyTorch, TensorFlow, JAX).
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.
- Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
- Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
- Exercising sound judgment.
Tags & Focus Areas
Machine Learning Ai
About TikTok
Responsible for the NLP-related research and development, mainly including: Building the intent classifier in a full-stack manner by designing taxonomy, creating and managing high quality labeled data, training the best performed machine learning model (like Bert, GPT), and monitoring the online performance of the model. Building the prediction model by mapping the shopping journey of our users to chatbot actions, including suggesting common question-answers and transferring the user to hum...
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