Machine Learning Engineer - E-Commerce Recommendation Live

What you'll do

• Scale recommendation foundation models and convert offline improvements into online results under latency and compute constraints.
• Develop multimodal generative retrieval, cross-domain recommendation, reward models, and LLM-agent optimization for live commerce.
• Own end-to-end work spanning problem framing, data pipelines, training, serving, A/B testing, and launch.

What they ask for

• Bachelor's degree or above in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.
• 3+ years of industry experience in recommendation, search, advertising, or other large-scale applied machine learning, with shipped models that improved online metrics.
• Strong machine learning fundamentals and hands-on depth in at least one listed area: retrieval and ranking, sequential user modeling, multi-task learning, generative recommendation, or LLM / multimodal modeling.
• Proficiency in Python and/or C++, and solid experience with PyTorch or TensorFlow, including distributed training and model optimization.

Nice to have

• Experience in e-commerce or live-streaming recommendation, ads ranking, or other real-time, conversion-driven systems.
• Experience with large recommendation models, generative recommendation, LLM / MLLM post-training, LLM agents, or large-scale training and inference efficiency.

Tools

Python, C++, PyTorch, TensorFlow, SFT, DPO, GRPO, RLHF

AI focus

Develop and apply large recommendation models, multimodal and generative recommendation, reward models and RL post-training, and LLM agents for live-commerce ranking, retrieval, and system optimization.