Machine Learning Engineer - Global E-commerce Recommendation/Search

TikTok Inc.; Seattle (WA); On-site; USD 154,000 - 301,000; Full time

Job summary

TikTok Inc. is seeking researchers for the Data-E-commerce team to advance retrieval and recommendation algorithms across our e‑commerce products.

You will help build scalable systems that surface relevant goods in feeds, live streams, and video contexts, while ensuring safety and trust across platforms. You will collaborate to develop long and short term user interest models, mine insights from vast data, and apply ML techniques to improve search, language understanding, and content safety in a

Qualifications

• BS/MS/PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Excellent analytical and problem‑solving skills.
• Strong foundation in machine learning and deep learning, with experience in NLP.
• Personalization.
• Exceptional coding skills with solid knowledge of data structures and algorithms.
• Proficiency in Linux development environments.

Responsibilities

• Work in a team to conduct cutting‑edge research in machine learning algorithms, such as retrieval and recommendation algorithms.
• Participate in building large‑scale (10 million to 100 million) e‑commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in TikTok.
• Build long and short term user interest models, analyze and extract relevant information from large amounts of data and design algorithms to explore users' latent interests efficiently.
• Apply machine learning algorithms to improve the different business scenarios, such as search ranking, natural language and video understanding, and trust and safety.

Skills

Machine Learning

Deep Learning

NLP

Programming (CS)

Linux development

Problem solving

Analytical thinking

Personalization

Education

BS/MS/PhD in Software Development/CS/CE

Tools

Git

Job description

Responsibilities

The Data-E-commerce team oversees algorithmic and corresponding engineering work in the e-commerce business. Our responsibilities encompass not only utilizing recommendation and search algorithms to help users discover appealing products but also implementing risk control and content safety algorithms to identify malicious sellers/influencers and violations in products/videos/live streams, ensuring a secure shopping experience. Furthermore, we are involved in constructing intelligent customer service technology and large-scale product knowledge graphs, aiming to enhance efficiency across various transaction processes. Through operations optimization and intelligent supply chain algorithms, we work to improve logistics efficiency.

• Work in a team to conduct cutting‑edge research in machine learning algorithms, such as retrieval and recommendation algorithms.
• Participate in building large‑scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in TikTok.
• Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently.
• Apply machine learning algorithms to improve the different business scenarios, such as search ranking, natural language and video understanding, and trust and safety.

Qualifications

Minimum Qualifications

• BS/MS/PhD background in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Excellent analytical and problem‑solving skills.
• Strong foundation in machine learning and deep learning, with experience in: NLP (Natural Language Processing).
• Personalization.
• Exceptional coding skills with solid knowledge of data structures and algorithms.
• Proficiency in Linux development environments.

Preferred Qualifications

• Prior experience in search, recommendation, or advertisement algorithms.
• Familiarity e-commerce businesses.

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;
3. Exercising sound judgment.

Responsibilities

The Data-E-commerce team oversees algorithmic and corresponding engineering work in the e-commerce business. Our responsibilities encompass not only utilizing recommendation and search algorithms to help users discover appealing products but also implementing risk control and content safety algorithms to identify malicious sellers/influencers and violations in products/videos/live streams, ensuring a secure shopping experience. Further, we are involved in constructing intelligent customer service technology and large-scale product knowledge graphs, aiming to enhance efficiency across various transaction processes. Through operations optimization and intelligent supply chain algorithms, we work to improve logistics efficiency.

• Work in a team to conduct cutting‑edge research in machine learning algorithms, such as retrieval and recommendation algorithms.
• Participate in building large‑scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in TikTok.
• Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently.
• Apply machine learning algorithms to improve the different business scenarios, such as search ranking, natural language and video understanding, and trust and safety.

Qualifications

Minimum Qualifications

• BS/MS/PhD background in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Excellent analytical and problem‑solving skills.
• Strong foundation in machine learning and deep learning, with experience in: NLP (Natural Language Processing).
• Personalization.
• Exceptional coding skills with solid knowledge of data structures and algorithms.
• Proficiency in Linux development environments.

Preferred Qualifications

• Prior experience in search, recommendation, or advertisement algorithms.
• Familiarity e-commerce businesses.

Qualifications