Machine Learning Engineer, Recommendations - USDS
Responsibilities
About the Team: We are a group of applied machine learning engineers and data scientists focusing on general feed recommendations and e-commerce recommendations. We develop innovative algorithms and techniques to improve user engagement and satisfaction, turning creative ideas into business-impacting solutions. We apply large-scale machine learning to solve real-world problems.
What you will do:
- 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 large volumes of diverse 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 real-time data pipelines, feature engineering, model optimization and innovation.
- Design and build supporting/debugging tools as needed.
In order to enhance collaboration and cross-functional partnerships, our 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 may change over time.
Qualifications
Minimum Qualifications
- Bachelor\'s degree or higher in Computer Science or related fields.
- Strong programming and problem-solving ability.
- Experience in applied machine learning with algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, Wide and Deep, etc.
- Experience with Deep Learning frameworks such as TensorFlow or PyTorch.
- Experience with at least one programming language like C++ or Python (or equivalent).
Preferred Qualifications
- Experience in recommendation systems, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields.
- Publications at KDD, NeurIPS, WWW, SIGIR, WSDM, ICML, IJCAI, AAAI, RECSYS or experience in data mining/machine learning competitions (e.g., Kaggle).
About USDS
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-first division focuses on data protection policies and content assurance to keep U.S. users safe. The teams within USDS span Trust & Safety, Security & Privacy, Engineering, User & Product Ops, Corporate Functions, and more.
Data Security Statement: This role requires handling systems designed to protect sensitive data and information and may be subject to national security-related screening.
Why Join Us
Our mission is to inspire creativity and bring joy. We value curiosity, humility, and impact, and foster an "Always Day 1" mindset to achieve meaningful breakthroughs for our users and our company.
Diversity & Inclusion
TikTok is committed to an inclusive space where employees are valued for their skills, experiences, and perspectives. We celebrate diverse voices and strive to reflect the communities we reach.
USDS Reasonable Accommodation
USDS provides reasonable accommodations in our recruitment processes for candidates with disabilities or other protected reasons. If you need assistance, please reach out at
Job Information
The base salary range for this position in the selected city is $136,800 - $359,720 annually. Compensation may vary based on qualifications, skills, and location. Base pay may be eligible for bonuses, incentives, and stock units. Benefits include medical, dental, vision, 401(k) with match, parental leave, disability coverage, life insurance, wellbeing benefits, and paid time off. Benefits may vary by location.
For Los Angeles County candidates: Qualified applicants with arrest or conviction records will be considered in accordance with applicable laws including the Fair Chance Act. Our company acknowledges that criminal history may impact certain job duties.
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