Machine Learning Engineer Intern (E-Commerce Recommendation Mall) - 2027 Start (PhD)

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

Benefits offered by this job

Health insurance

Wellbeing benefits

Housing allowance

10 paid holidays

Paid sick time

Job summary

TikTok is offering a PhD internship for the E-commerce Recommendation Team in San Jose. You will conduct cutting-edge research in generative recommender systems, LLM-based enhancements, and agentic architectures to improve personalization and efficiency on TikTok's shopping experiences.

You will collaborate with industry experts, publish insights, and contribute to next-generation algorithms aimed at scalable, long-term value.

Qualifications

• Currently pursuing a PhD in Computer Science, Electrical Engineering, Mathematics, Statistics or related field.
• Strong foundation in ML/DL with research or project experience in LLMs, RL, generative models, or recommender systems.
• Proficient in Python and at least one DL framework (PyTorch, TensorFlow, or JAX).

Responsibilities

• Generative Recommendation Research: advance recommender systems with generative methods and optimize training/inference on GPUs.
• LLM for Recommendation Research: use LLMs/RL to improve semantic understanding and reasoning in recommendations.
• Agentic Recommendation Research: explore self-evolving agents for real-time personalized decisions.
• Long-Term Value and User Experience Modeling: develop algorithms to measure and optimize LTV and user experience.

Skills

Python programming

Machine learning

Deep learning

PyTorch

TensorFlow

JAX

Education

PhD student in CS/EE/Math/Stats

Tools

PyTorch

TensorFlow

JAX

Job description

Location:

San Jose

Employment Type:

Intern

Job Code:

A258406B

Team Introduction

Our E-commerce Recommendation Team is responsible for building up and scaling our recommendation system to provide the best shopping experience for our TikTok users. We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands‑on learning, enriching community‑building and professional development events, and collaboration with industry experts.

Responsibilities

• Generative Recommendation Research: Drive the evolution of recommender systems from discriminative to generative paradigms; explore frontier directions such as generative retrieval and generative re-ranking/blending; continuously improve personalization capabilities while deeply optimizing the training and inference efficiency of generative models on GPUs.
• LLM for Recommendation Research: Leverage Large Language Models (LLMs), Reinforcement Learning (RL), and related techniques to enhance the semantic understanding and reasoning capabilities of recommender systems, addressing core business challenges in e-commerce scenarios (e.g., cold start, long-tail item distribution, and user intent understanding).
• Agentic Recommendation Research: Explore the construction of self-evolving agents and leverage agents to continuously optimize recommender systems; drive the evolution of recommender systems toward agentic architectures capable of keenly perceiving user context and making real-time, personalized decisions and adjustments.
• Long-Term Value and User Experience Modeling: Explore replacing traditional heuristic rule-based systems with LLM and agent capabilities; build next-generation algorithms for measuring and optimizing long-term value (LTV) and user experience, enabling sustainable growth of the platform ecosystem.

Minimum Qualifications

• Currently pursuing a PhD in in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline.
• Solid foundation in machine learning and deep learning, with research or project experience in at least one of the following areas: large language models, reinforcement learning, generative models, recommender systems or information retrieval.
• Proficient in Python and at least one mainstream deep learning framework (e.g., PyTorch, TensorFlow, JAX). Strong problem-solving skills and passion for tackling complex, open-ended research problems.

Preferred Qualifications

• Experience in recommendation systems, especially in live commerce, e-commerce, search, ads, or other large-scale consumer products.
• Experience with generative recommendation, large recommendation models, retrieval and ranking systems, or related recommendation architecture upgrades.
• Experience with LLMs or multimodal foundation models, including pre-training, post-training, representation learning, contrastive learning, SFT, or RL-based optimization.
• Experience with cross-domain transfer learning, LTV modeling, long-term value optimization, causal inference, or debiasing.
• Experience with long-sequence user behavior modeling, multi-task learning, multi-interest matching, or large-scale distributed training and inference optimization.
• Publications in top-tier conferences such as NeurIPS, ICML, ICLR, KDD, ACL, CVPR, SIGIR, or RecSys, or strong achievements in major technical competitions.
• Strong curiosity about new technologies, fast learning ability, and a passion for solving challenging real-world problems.

Job Information

The hourly rate range for this position in the selected city is $60- $60.

• Benefits may vary depending on the nature of employment and the country work location.
• Interns have day one access to health insurance, life insurance, wellbeing benefits and more.
• Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year).
• Interns who are not working 100% remote may also be eligible for housing allowance.

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3. Exercising sound judgment.

About TikTok

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles