Machine Learning Engineer Graduate (Recommendations, USDS) - 2025 Start (MS) The base pay range for this position is described in the job post as $118,657.00/yr - $187,200.00/yr. Compensation may vary outside this range depending on factors including qualifications, skills, competencies and experience, and location. Base pay is part of the total package and may include additional discretionary bonuses/incentives and restricted stock units.

Responsibilities

Participate in building large-scale (10 million to 100 million) recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations, etc.

Build long and short term user interest models, analyze and extract relevant information from large amounts of data, and design algorithms to explore users' latent interests efficiently.

Design, develop, evaluate and iterate on predictive models for candidate generation and ranking (e.g., Click Through Rate and Conversion Rate prediction), including building real-time data pipelines, feature engineering, model optimization and innovation.

Design and build supporting/debugging tools as needed.

To enhance collaboration and cross-functional partnerships, the organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. The hybrid model is reviewed regularly and requirements may change.

Qualifications Minimum Qualifications

PhD or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline.

0-1 years of experience in machine learning, deep learning, data mining, or artificial intelligence.

Proficient in programming languages such as Python, C++, Java, or similar.

Preferred Qualifications

Deep understanding of recommendation algorithms and personalization systems.

Excellent problem-solving and analytical skills.

Strong ability to communicate complex ideas effectively to both technical and non-technical audiences.

Experience with reinforcement learning techniques.

Proven modeling/algorithms competition records on Kaggle or top conferences’ challenges.

Proven programming competition records on ICPC, IOI or USACO.

Experience working with recommendation systems, computational advertising, search engine, E-commerce recommendation systems.

Publications in machine learning or related conferences or journals are highly desirable.

About the role and company TikTok is the leading destination for short-form mobile video. U.S. Data Security (USDS) is a subsidiary of TikTok in the U.S. This security-focused division focuses on data protection policies and content assurance protocols to protect U.S. user data. The teams within USDS span Trust & Safety, Security & Privacy, Engineering, User & Product Ops, and more.

Data Security Statement: This role requires ability to work with and support systems designed to protect sensitive data and information and may be subject to strict national security-related screening.

Why Join Us We aim to inspire creativity and bring joy through an innovative product. We value curiosity, humility, and impact, and we continuously iterate to achieve meaningful breakthroughs for our users and the company.

Equity, diversity and inclusion are core to our culture. We are committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities or other protected reasons, and we offer a range of benefits that may vary by location.

Job Information Seniority level: Internship

Employment type: Full-time

Job function: Engineering and Information Technology

Industries: Entertainment Providers

Location: San Jose, CA (or related area)

#J-18808-Ljbffr