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

What you'll do

• Develop large-scale recommendation models and generative retrieval, including training, serving, and low-latency decoding.
• Apply large models and reasoning to user-interest discovery, and optimize recommendation training and inference for hardware performance.
• Use and build coding-agent workflows; conduct original research on recommendation problems and deliver systems 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 model mathematics and clean, efficient, reproducible code.
• Deep research or engineering experience in at least one of LLMs/foundation models, NLP, CV, RL, recommendation, search, or ads.
• Strong interest in putting LLM/LRM techniques into production and ability to define and decompose ambiguous problems.

Nice to have

• Publications at listed or comparable venues, or high-quality open-source work.
• Experience with CUDA/Triton, distributed training, high-performance inference, LLM post-training, agent systems, or a relevant end-to-end project.

Tools

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

AI focus

Build LLM-based recommendation models and generative retrieval; use model knowledge and reasoning for user-interest discovery; apply agents to algorithm development and experimentation.