2 jobs for "Research Scientist Graduate (E-Commerce Recommendation Region) - 2027 Start"

Research Scientist Graduate (E-Commerce Recommendation Region) - 2027 Start (PhD)

At TikTok - Seattle

Posted on Oct 14

View job Apply on company site

Research Scientist Graduate (E-Commerce Recommendation Region) - 2027 Start

At TikTok - Seattle

Posted on Oct 14

View job Apply on company site

TikTok

Research Scientist Graduate (E-Commerce Recommendation Region) - 2027 Start (PhD)

Seattle$153,900 – $300,960Posted 27 days ago

Apply on company site View full job

Responsibilities

We build the recommendation systems behind TikTok Shop's shoppable videos and LIVE streams in fast-growing markets across Latin America, Canada, Asia, Australia and the new countries we launch each year. AI and LLMs are transforming e-commerce, recommender systems, and the way research scientists work, and they are opening up new problems and opportunities at the frontier.
LLMs and agents are core to how we work, not just tools we reach for. Our autonomous research agents already run experiments, tune models and diagnose issues in production, and they drive a remarkable share of the improvements we ship.

As more of the execution is automated, what matters most is knowing what to solve. Beyond building and scaling generative models, we own business outcomes end to end: we find the problems, set the direction and define the goals.

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 e-commerce recommendation at global scale, end to end. Design and improve the recommendation systems for a platform serving over a billion users, across shoppable videos, LIVE streams and products. You will work on every stage, from retrieval and pre-ranking to ranking and final blending.
- Pioneer generative and LLM-powered recommendation. Use LLMs and generative models to change how recommendation works: generative retrieval, LLM reasoning about user intent (LLM4Rec), and multimodal understanding of AI-generated content (AIGC).
- Optimize decisions with RL, causal inference and operations research. Frame traffic allocation, buyer acquisition and compute allocation as rigorous optimization problems. Use reinforcement learning, causal and uplift modeling, and constrained optimization to decide where each impression and each unit of compute creates the most incremental value.
- Build agents that do research. Create autonomous research agents that mine data for insights, search across competing objectives to push the Pareto frontier, run and analyze experiments, and diagnose production issues. These agents turn weeks of iteration into hours and change how applied science gets done.
- Model the full lifetime of a shopper. Look beyond the next click: model long-term value, repurchase and retention, purchase attribution across surfaces and over time, content fatigue, new-user recommendation and interest exploration. The goal is a system that builds lasting relationships with users, not one-off transactions.
- Turn research into impact. Own your ideas from first prototype to production, validate them through large-scale online experiments, and share what you learn through top-tier publications.

Qualifications

Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD degree in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline.
- Strong foundations in machine learning and its underlying math (probability, statistics, optimization), along with solid algorithms and data structures.
- Excellent coding skills in Python or C++ and hands-on experience with deep learning frameworks such as PyTorch.
- Research or project experience in at least one of: recommendation, search or ads; LLMs and generative models; reinforcement learning; causal inference; operations research and optimization; multimodal learning; or AI agents.
- A proven ability to take an open-ended, loosely defined problem, frame it precisely, and drive it to a measurable result.

Preferred Qualifications:
- First-author publications at top venues (NeurIPS, ICML, ICLR, KDD, SIGIR, RecSys, WWW, WSDM, ACL, CVPR, EC) or impactful open-source work.
- Experience with generative recommendation, LLM4Rec, or LLM post-training (SFT, DPO, GRPO, RL-based fine-tuning).
- Experience building LLM agents or automating research workflows, and heavy use of AI coding tools.
- Background in reinforcement learning or bandits, causal and uplift modeling, constrained optimization
- Experience modeling long-term user value, such as retention, user lifetime value or sequential user behavior.
- Experience shipping ML models to production or running large-scale online experiments, for example through internships at consumer-scale products.
- Outstanding results in competitions such as ICPC, Kaggle or KDD Cup

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $153900 - $300960 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.

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.

Responsibilities

Qualifications

Qualifications

Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD degree in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline.
- Strong foundations in machine learning and its underlying math (probability, statistics, optimization), along with solid algorithms and data structures.
- Excellent coding skills in Python or C++ and hands-on experience with deep learning frameworks such as PyTorch.
- Research or project experience in at least one of: recommendation, search or ads; LLMs and generative models; reinforcement learning; causal inference; operations research and optimization; multimodal learning; or AI agents.
- A proven ability to take an open-ended, loosely defined problem, frame it precisely, and drive it to a measurable result.

Preferred Qualifications:
- First-author publications at top venues (NeurIPS, ICML, ICLR, KDD, SIGIR, RecSys, WWW, WSDM, ACL, CVPR, EC) or impactful open-source work.
- Experience with generative recommendation, LLM4Rec, or LLM post-training (SFT, DPO, GRPO, RL-based fine-tuning).
- Experience building LLM agents or automating research workflows, and heavy use of AI coding tools.
- Background in reinforcement learning or bandits, causal and uplift modeling, constrained optimization
- Experience modeling long-term user value, such as retention, user lifetime value or sequential user behavior.
- Experience shipping ML models to production or running large-scale online experiments, for example through internships at consumer-scale products.
- Outstanding results in competitions such as ICPC, Kaggle or KDD Cup

About TikTok

TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.

Size: 5,001 - 10,000 employees
Industry: Entertainment & Gaming

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