6 jobs for "Machine Learning Engineer Graduate (E-Commerce Recommendation Foundation) - 2027 Start"

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

At TikTok - Seattle

Posted on Aug 12

View job Apply on company site

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

At TikTok - San Jose

Posted on Aug 12

View job Apply on company site

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

At TikTok - San Jose

Posted on Aug 12

View job Apply on company site

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

At TikTok - Seattle

Posted on Aug 12

View job Apply on company site

Machine Learning Engineer Graduate (Global E-Commerce) - 2027 Start (PhD)

At TikTok

Posted on Oct 2

View job Apply on company site

AI Infra Optimization Engineer Graduate (TikTok Global E-Commerce Recommendation & Search Architecture) - 2027 Start

At TikTok

Posted on Aug 3

View job Apply on company site

TikTok

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

Seattle$153,900 – $300,960Posted Aug 12, 2026

Apply on company site View full job

Why you should apply for a job to TikTok:

4.5/5 in overall job satisfaction
4.5/5 in supportive management
100% say women are treated fairly and equally to men
100% would recommend this company to other women
100% say the CEO supports gender diversity

Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›

Responsibilities

The Recommendation Foundation team within TikTok's Data - Global E-commerce organization is dedicated to building shared Recommendation Foundation Models across scenarios. We are exploring an event-sequence-driven generative recommendation paradigm that deeply integrates large language and vision-language models (LLMs/VLMs), multimodal understanding, reinforcement learning, and system optimization, advancing recommendation systems beyond click prediction toward general-purpose recommendation agents.

We believe the future of recommendation is not only about predicting clicks, but about understanding the relationships between people and content and generating new connections. We value original exploration and encourage research thinking and engineering practice equally. Every team member can propose hypotheses and validate ideas in an open environment; your code and publications may help shape the next generation of recommendation systems. We are looking for people with a general-intelligence mindset to redefine recommendation with us.

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:
1. Participate in the full training lifecycle of Recommendation Foundation Models, including pre-training, mid-training, and post-training.
2. Design and train multimodal semantic tokenizers for recommendation items, leveraging multimodal foundation models to encode rich item content into discrete semantic tokens and raise the performance ceiling of Recommendation Foundation Models.
3. Develop LLM-native recommendation by incorporating recommendation tasks directly into large language model training and leveraging world knowledge to improve recommendation quality.
4. Build the next generation of recommendation systems powered by Recommendation Foundation Models, spanning retrieval, ranking, and end-to-end generative recommendation.

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.
- Solid foundation in machine learning and deep learning, with strong interest in LLMs and generative recommendation.
- Proficiency in Python and experience with deep learning frameworks such as PyTorch.
- Self-driven, with a strong research mindset and solid engineering skills.

Preferred Requirement:
- Experience with pre-training, mid-training, or post-training of LLMs or Foundation Models.
- Research or project experience in generative recommendation, LLM-native recommendation, or multimodal semantic tokenization.
- Publications on LLM-related topics at top-tier machine learning or natural language processing conferences, such as NeurIPS, ICML, ICLR, ACL, EMNLP, or NAACL, or strong achievements in major technical competitions.

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.
- Solid foundation in machine learning and deep learning, with strong interest in LLMs and generative recommendation.
- Proficiency in Python and experience with deep learning frameworks such as PyTorch.
- Self-driven, with a strong research mindset and solid engineering skills.

Preferred Requirement:
- Experience with pre-training, mid-training, or post-training of LLMs or Foundation Models.
- Research or project experience in generative recommendation, LLM-native recommendation, or multimodal semantic tokenization.
- Publications on LLM-related topics at top-tier machine learning or natural language processing conferences, such as NeurIPS, ICML, ICLR, ACL, EMNLP, or NAACL, or strong achievements in major technical competitions.

About TikTok

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

Size: 10,000+ employees
Industry: Technology: Consumer Internet

View company profile