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

What you'll do

• Develop and scale end-to-end recommendation models and generative retrieval systems for production.
• Optimize training and inference, including GPU kernels, distributed systems, and low-bit quantization.
• Research recommendation problems and build agent-assisted R&D workflows; contribute original research, papers, or patents.

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, data structures, and 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 deploying LLM/LRM methods in 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 with CUDA/Triton, distributed training, LLM post-training, agent systems, inference acceleration, or relevant end-to-end projects.

Tools

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

AI focus

Build LLM-based recommendation and generative retrieval, apply reasoning models to user intent, optimize model training and inference, and use coding agents in the R&D workflow.