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

Centro Escolar University; Seattle (WA); On-site; USD 34,000 - 55,000; Part time

Job summary

Centro Escolar University is seeking an undergraduate student in software development or computer science to join our live commerce recommendation team. You will build and optimize models spanning recall to ranking, and explore cross-domain and multimodal signals across videos, live streams, and products.

The role emphasizes scalable experimentation and translating research into tangible business impact. The ideal candidate has strong Python/C++ programming skills, experience with PyTorch, and a

Qualifications

• Pursuing an undergraduate degree in a software-related field.
• Solid grounding in machine learning concepts and methods.
• Strong programming skills in Python or C++ and hands-on DL experience.
• Familiarity with data structures, algorithms, and large-scale ML systems.
• Good problem-solving, analytical mindset, and ownership of model iterations.

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 connecting videos, live streams, products, and user behavior for live commerce recommendations.
• Advance next-generation recommender tech including generative models, large recommendation models, RL, and long-term value optimization.
• Collaborate with cross-functional teams to launch scalable solutions, run experiments, and translate research into measurable business impact.

Skills

Python

C++

PyTorch

Machine Learning

Education

Undergraduate student in Software Development/CS

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.