Applied ML Research Intern — Recommender & Multi-Modal AI

ByteDance; San Jose (CA); On-site; USD 68,000 - 97,000; Part time

Benefits offered by this job

Health insurance

Life insurance

Wellbeing benefits

Paid holidays

Housing allowance

Job summary

ByteDance is seeking a campus intern for its Applied Machine Learning team in the Los Angeles area. You will work on state-of-the-art recommender, ads, and search solutions, contributing to the full ML lifecycle from ideation to production.

The role emphasizes strong ML theory and practice, with opportunities to explore large-scale models, multi-modal data, and RL for personalization. PhD candidates in related fields are welcome to apply.

Qualifications

• Pursuing a PhD in a technical field related to ML.
• Strong research background in ML, DL, or recommender systems.
• Proficiency in Python or C++, with DL frameworks.

Responsibilities

• Research ML methods for recommendations, ads, and search in production-scale systems.
• Prototype multi-modal modeling strategies using text, image, and video data.
• Explore long-term user behavior modeling and RL techniques for engagement.
• Collaborate with mentors and engineers to test ideas in real environments.
• Share findings through internal talks, reports, or publications.

Skills

ML research

Deep learning

Recommender systems

Python/C++

PyTorch/TensorFlow

Education

PhD student in CS/CE/EE

Tools

PyTorch

TensorFlow

Python

C++

Job description

ByteDance is seeking a campus intern for its Applied Machine Learning team in the Los Angeles area. You will work on state-of-the-art recommender, ads, and search solutions, contributing to the full ML lifecycle from ideation to production.

The role emphasizes strong ML theory and practice, with opportunities to explore large-scale models, multi-modal data, and RL for personalization. PhD candidates in related fields are welcome to apply.