Machine Learning Engineer Intern (E-Commerce Recommendation Live)- 2027 Summer

UEC; Seattle (WA); On-site; USD 34,000 - 62,000; Full time

Job summary

UEC in Seattle is seeking an enthusiastic undergraduate student to join our software/ML team. You will build and optimize recommendation models across recall, pre-ranking, ranking, and mixed ranking to improve GMV, conversion, watch time, and long-term user value.

You'll work on cross-domain and multimodal modeling, connect videos, live streams, products, and user behavior, and advance next-generation techniques including generative recommendations and large models.

Qualifications

• Currently pursuing an undergraduate degree in Software Development, Computer Science, Computer Engineering, or a related discipline.
• Solid foundation in machine learning and expertise in at least one area such as recommendation systems, search, advertising, NLP, multimodal learning, or large-scale applied AI.
• Strong programming skills in Python or C++ and hands-on experience with deep learning frameworks like PyTorch.
• Good understanding of data structures, algorithms, and large-scale model training or production machine learning systems.
• Strong analytical and problem-solving skills with the ability to translate business problems into effective modeling solutions.
• Self-driven and results-oriented with ownership of model iteration and online impact from end to end.

Responsibilities

• Build and optimize recommendation models across recall, pre-ranking, ranking, and mixed ranking to improve GMV, conversion, watch time, and long-term user value.
• Develop cross-domain and multimodal modeling solutions connecting videos, live streams, products, and user behavior to enhance live commerce recommendations.
• Advance next-generation recommendation technologies including generative recommendation, large recommendation models, reinforcement learning, and long-term value optimization.
• Collaborate with cross-functional teams to launch scalable solutions, conduct experiments, and translate research into measurable business impact.

Skills

ML fundamentals

Python

PyTorch

C++

Data structures

Algorithms

Online experimentation

Problem solving

Cross-domain modeling

Education

Undergraduate student (CS/CE)

Tools

PyTorch

Job description

Your Role

Here’s what you will be doing:

• Build and optimize recommendation models across recall, pre-ranking, ranking, and mixed ranking to improve GMV, conversion, watch time, and long-term user value.
• Develop cross-domain and multimodal modeling solutions connecting videos, live streams, products, and user behavior to enhance live commerce recommendations.
• Advance next-generation recommendation technologies including generative recommendation, large recommendation models, reinforcement learning, and long-term value optimization.
• Collaborate with cross-functional teams to launch scalable solutions, conduct experiments, and translate research into measurable business impact.

About You

The company is looking for candidates who meet the following qualifications:

• Currently pursuing an undergraduate degree in Software Development, Computer Science, Computer Engineering, or a related discipline.
• Solid foundation in machine learning and expertise in at least one area such as recommendation systems, search, advertising, NLP, multimodal learning, or large-scale applied AI.
• Strong programming skills in Python or C++ and hands-on experience with deep learning frameworks like PyTorch.
• Good understanding of data structures, algorithms, and large-scale model training or production machine learning systems.
• Strong analytical and problem-solving skills with the ability to translate business problems into effective modeling solutions.
• Self-driven and results-oriented with ownership of model iteration and online impact from end to end.

Preferred qualifications include experience in recommendation systems for live commerce or large-scale consumer products, generative recommendation, large recommendation models, retrieval and ranking systems, LLMs or multimodal foundation models, cross-domain transfer learning, long-term value optimization, causal inference, debiasing, long-sequence user behavior modeling, multi-task learning, multi-interest modeling, and large-scale distributed training and inference optimization. Publications in top-tier conferences or achievements in major technical competitions are also valued. A strong curiosity about new technologies, fast learning ability, and passion for solving challenging real-world problems are important.