Machine Learning Engineer Intern (E-Commerce Recommendation Video) - 2027 Start (PhD）

What you'll do

• Develop and scale recommendation models and generative retrieval for video, livestream, and product recommendations.
• Optimize recommendation training and inference for performance and latency, including distributed systems and hardware-level acceleration.
• Conduct original research on recommendation problems and build agent-assisted R&D workflows.

What they ask for

• Currently pursuing a PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related discipline.
• Strong machine-learning and engineering fundamentals, including algorithms and data structures and the ability to write clean, efficient, reproducible code.
• Deep research or engineering practice in at least one of LLMs/foundation models, NLP, CV, RL, recommendation, search, or ads.
• Enthusiasm for bringing LLM/LRM techniques into production 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 relevant end-to-end projects.

Tools

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

AI focus

Build LLM-based recommendation systems, including generative retrieval, large-scale recommendation models, LLM post-training, reasoning over user intent, and agent-assisted research and development.