Research Intern: AML & Personalization Algorithms

ByteDance; San Jose (CA); On-site; USD 68,191 - 97,120; Part time

Benefits offered by this job

Health insurance

Life insurance

Wellbeing benefits

Housing allowance

10 paid holidays per year

Paid sick time

Job summary

ByteDance is seeking a PhD candidate for a Research Intern (AML-Algorithm) in San Jose. You will conduct ML research for recommendations, prototype multi‑modal modeling, and explore RL for personalization with mentorship from senior researchers.

You will contribute to long‑term user behavior modeling, collaborate with teams, and disseminate findings via talks and reports. This internship offers exposure to large-scale ML systems and real‑world deployment considerations.

Qualifications

• Pursuing a Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or related technical field.
• Strong research background in machine learning, deep learning, recommender systems, or related areas.
• Proficiency in Python or C++, and familiarity with PyTorch or TensorFlow.
• Understanding of transformers, LLMs, or multi-modal learning.
• Ability to conduct independent research with strong problem-solving and analytical skills.
• Preferred: Publications in top ML/AI conferences (e.g., NeurIPS, ICML, ICLR, KDD, RecSys, WWW).
• Preferred: Experience with large-scale ML systems or end-to-end ML pipelines.
• Preferred: Passion for applying research to real-world challenges in recommendation, personalization, and user experience.

Responsibilities

• Conduct research in machine learning for recommendation systems, with opportunities to explore large-scale recommendation models, generative recommendation, or reinforcement learning for personalization.
• Explore and prototype new modeling strategies that leverage multi-modal data (e.g., text, image, video) to enhance content and user understanding.
• Investigate long-term user behavior modeling and reinforcement learning techniques to improve sustained engagement.
• Collaborate with mentors and other researchers/engineers to test ideas in real-world environments.
• Share findings through internal presentations, technical reports, and potentially external publications.

Skills

Python

C++

Machine learning

Deep learning

Recommender systems

Multi-modal learning

Transformers

LLMs

PyTorch

TensorFlow

Education

PhD in CS/CE/EE or related field

Tools

PyTorch

TensorFlow

Job description

ByteDance is seeking a PhD candidate for a Research Intern (AML-Algorithm) in San Jose. You will conduct ML research for recommendations, prototype multi‑modal modeling, and explore RL for personalization with mentorship from senior researchers.

You will contribute to long‑term user behavior modeling, collaborate with teams, and disseminate findings via talks and reports. This internship offers exposure to large-scale ML systems and real‑world deployment considerations.