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

TikTok Inc.; San Jose (CA); On-site; USD 128,000 - 317,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with company match

Paid parental leave

Short-term and long-term disability

Life insurance

Wellbeing benefits

10 paid holidays

10 paid sick days

17 days of Paid Personal Time (prorata

Job summary

TikTok Inc. in San Jose, California, is seeking to join the Recommendation Foundation team within Data - Global E-commerce to explore event-sequence-driven generative recommendation.

The role integrates large language models, vision-language models, multimodal understanding, reinforcement learning, and system optimization to push beyond click prediction toward general-purpose recommendation agents. As a graduate, you will have opportunities to pursue bold ideas, tackle complex challenges, and

Qualifications

• Bachelor's 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.

Responsibilities

• Participate in the full training lifecycle of Recommendation Foundation Models, including pre-training, mid-training, and post-training.
• 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.
• Develop LLM-native recommendation by incorporating recommendation tasks directly into large language model training and leveraging world knowledge to improve recommendation quality.
• Build the next generation of recommendation systems powered by Recommendation Foundation Models, spanning retrieval, ranking, and end-to-end generative recommendation.

Skills

Bachelor's degree

ML/DL fundamentals

Python

PyTorch

Research mindset

Self-driven

Education

Bachelor's degree in CS/EE/Math/Stats

Tools

PyTorch

Job description

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.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

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 Bachelor's 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 Qualifications:

• 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 $128000 - $316800 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:

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.