Responsibilitie

You will be joining our Applied Machine Learning team, a central team responsible for delivering state-of-the-art solutions powering our company's recommendations, ads, and search systems across various products such as TikTok, Douyin. We own the end-to-end ML lifecycle, from ideation and research to building, deploying, and iterating on models in production. We are looking for candidates who are passionate about solving complex problems and have a strong foundation in machine learning theory and practice. Some of the projects we have been working on: - Large Scale Recommendation Models - End-to-End Generative Recommendation Systems - Reinforcement Learning for User Personalization in Recommendation Systems You Will: In this role, you will drive the next wave of innovation for our recommendation systems, directly shaping the user experience by: - Build and scale up machine learning models for recommendation systems - Research and apply multi-modal techniques (leveraging text, image, video) to create a holistic understanding of content and user preferences - Pioneer new modeling strategies by researching and integrating long-term user behavior signals to drive sustained engagement and satisfaction, by using techniques such as reinforcement learning - Partner closely with the infrastructure team to co-design and optimize next-generation recommendation model architectures and systems, ensuring high-performance, low-latency, and cost-efficient training and inference at a massive scale. - Work hand-in-hand with product, engineering, and design teams to rigorously test and deploy end-to-end solutions, validating their impact and ensuring they create a seamless and enhanced user experience.

Qualification

Minimum Qualifications: - A Bachelor's degree in Computer Science, Computer Engineering, or a related technical field is required. A Ph.D. in a relevant field is highly preferred. - At least 5 years of experience in proficiency in one or more programming languages such as Python or C++, and deep learning frameworks like PyTorch or TensorFlow. - Demonstrated expertise in designing, building, and scaling machine learning models for recommendation systems. - Deep understanding and hands-on experience with modern deep learning techniques, including Transformers, Large Language Models (LLMs), and multi-modal learning. - Proven experience in building and deploying end-to-end ML pipelines in a production environment. - A track record of publications at accredited peer-reviewed conferences such as NeurIPS, ICML, ICLR, KDD, RecSys, WWW

Job Information

【For Pay Transparency】Compensation Description (Annually)

The base salary range for this position in the selected city is $162000 - $387600 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

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