Machine Learning Engineer Graduate (E-Commerce Content Recommendation - Generative & Large Recommendation Model) - 2027 Start (PhD)

What you'll do

• Develop and scale recommendation models and generative retrieval for short-video, livestream, and product scenarios.
• Optimize model training and inference, including kernels, distributed systems, and millisecond-latency serving.
• Research recommendation problems and build agent-assisted R&D workflows; deploy iterations to production.

What they ask for

• Completing or recently completed a PhD in Computer Science, AI, Mathematics, Statistics, or a related discipline.
• Strong machine-learning and engineering fundamentals, including algorithms, data structures, and clean, efficient, reproducible code.
• Deep research or engineering practice in at least one of LLMs/foundation models, NLP, CV, RL, or recommendation/search/ads.
• Genuine enthusiasm for production use of LLM/LRM techniques and ability to define and decompose ambiguous problems.

Nice to have

• Publications at listed venues or comparable venues, or high-quality open-source work.
• Experience in CUDA/Triton, distributed training, high-performance inference, LLM post-training, agent systems, or related projects.

Tools

LLMs, RQ-VAE, SID, CUDA, Triton, GRPO, SFT, RLHF

AI focus

Build LLM-based and generative recommendation models, apply large-model knowledge and reasoning to user-interest discovery, and use coding agents to automate parts of R&D.