Machine Learning Engineer Graduate (E-Commerce Recommendation Live) - 2027 Start

Seattle, WA, United States · Regular

About the role

The Global E-commerce Recommendation Live Algorithm team is responsible for the core recommendation stack for live commerce, covering the full pipeline from recall and pre-ranking to ranking and mixed ranking. The team operates in a highly dynamic environment where live room status changes in real time, conversion signals are sparse, and user intent must be understood across content, commerce, and transaction scenarios.

By combining generative recommendation, large recommendation models, multimodal representation learning, and cross-domain value modeling, the team works on some of the most important algorithmic problems in live commerce. Our goal is to improve user experience, optimize ecosystem efficiency, and drive sustainable business growth for TikTok Shop across global markets.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.

Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities

• Build and optimize recommendation models across recall, pre-ranking, ranking, and mixed ranking to improve GMV, conversion, watch time, and long-term user value.
• Develop cross-domain and multimodal modeling solutions that connect videos, live streams, products, and user behavior to better power live commerce recommendations.
• Advance next-generation recommendation technologies, including generative recommendation, large recommendation models, reinforcement learning, and long-term value optimization.
• Partner with cross-functional teams to launch scalable solutions, run experiments, and turn research into measurable business impact.

Qualifications

Minimum Qualifications

• Individuals who are completing or have recently completed a Bachelor’s degree in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline.
• Solid foundation in machine learning and at least one of the following areas: recommendation systems, search, advertising, NLP, multimodal learning, or large-scale applied AI.
• Strong programming skills in Python or C++, and hands-on experience with deep learning frameworks such as PyTorch.
• Good understanding of data structures, algorithms, and large-scale model training or production machine learning systems.
• Strong analytical and problem-solving skills, with the ability to translate business problems into effective modeling solutions.
• Self-driven and results-oriented, with the ability to take ownership of model iteration and online impact from end to end.

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 in 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 modeling, 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.

Pay

The base salary range for this position in Seattle is $121,600 - $243,200 annually. Compensation may vary outside of this range depending on qualifications, skills, competencies, experience, and location. Base pay is one part of the total package that may include additional discretionary bonuses/incentives and restricted stock units.

Benefits

• Day one access to medical, dental, and vision insurance.
• 401(k) savings plan with company match.
• Paid parental leave.
• Short-term and long-term disability coverage.
• Life insurance.
• Wellbeing benefits.
• 10 paid holidays per year.
• 10 paid sick days per year.
• 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

About TikTok

TikTok is the leading destination for short-form mobile video. Our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, with offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

We strive to do great things with great people. We lead with curiosity, humility, and a desire to make an impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We embrace an "Always Day 1" mindset to achieve meaningful breakthroughs for ourselves, our company, and our users.

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe, and so does our workplace. We are passionate about celebrating our diverse voices and creating an environment that reflects the many communities we reach.