Machine Learning Engineer - E-commerce Merchant and Creator Growth - San Jose

TikTok

Sourced from TikTok's careers site · verified 2 days ago

San Jose, California, US Full time 3+ years exp Company has sponsored H-1B before Posted March 29, 2026
Job description

Description

Our Team Supply Side Algorithms
Our team is committed to expanding the number of merchants and creators on TikTok Shop, as well as providing them with comprehensive support to foster growth within the TikTok Shop ecosystem. We achieve this by developing end-to-end algorithmic capabilities utilizing machine learning, data mining, and causal inference methodologies.

We are seeking a talented and motivated Machine Learning Engineer with expertise in marketplace growth to join our dynamic and fast-paced team. In this role, you will collaborate with cross-functional teams including data scientists, product managers, and business stakeholders to develop innovative solutions that drive the growth of merchants and creators in TikTok Shop.

Responsibilities
1. Utilize advanced machine learning techniques to analyze large-scale datasets and identify meaningful, correlations, and causal relations related to merchant and creator growth in TikTok Shop
2. Collaborate with business stakeholders, product managers, and data scientists to define data mining objectives and develop strategies to address complex business problems and opportunities.
3. Apply feature engineering techniques to derive relevant features and embeddings from raw data and improve the performance of machine learning models.
4. Develop scalable and efficient data pipelines to preprocess and transform data for machine learning tasks, ensuring data quality, consistency, and availability.
5. Evaluate and benchmark different machine learning approaches, algorithms, and tools, and recommend the most appropriate solutions based on performance, scalability, and interpretability.
6. Stay updated with the latest advancements in data mining, machine learning, and related fields, and apply this knowledge to enhance the team's capabilities and identify new opportunities.
7. Communicate findings, insights, and technical concepts effectively to both technical and non-technical stakeholders, fostering a collaborative and data-driven decision-making culture.

Requirements

Minimum Qualifications
1. Highly self-motivated to drive business growth and foster technical advancement.
2. Proficient in using SQL and Python and experience with data manipulation
3. Experience with big data processing frameworks (e.g., Hadoop, Spark) and distributed computing for efficient data mining on large-scale datasets.
4. Solid understanding of machine/deep learning concepts and techniques, including feature engineering, model evaluation, and optimization.
5. Strong analytical and problem-solving skills, with a demonstrated ability to handle and derive insights from complex and unstructured datasets.

Preferred Qualifications:
1. Master's or advanced degree in Computer Science, Data Science, Statistics, or a related field.
2. 3+ years experience as a Machine Learning Engineer, Data Scientist, and experience in causal machine learning is preferred
3. Work experience in user growth, marketing algorithms, recommendation algorithms, advertisement algorithms or related fields is preferred.
4. Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and convey technical concepts to non-technical stakeholders.

Skills

  • SQL
  • Python
  • big data processing
  • Hadoop
  • Spark
  • machine learning
  • deep learning
  • feature engineering
  • model evaluation
  • data manipulation
  • data mining
  • recommendation algorithms
About this role

Summary

Develop machine learning models and data pipelines to support marketplace growth on TikTok Shop.

Job title

Machine Learning Engineer

Experience

3+ years

Industry

software

Location

Cambrian, San Jose, CA

Salary

Pay not disclosed by employer

Visa sponsorship

Company has sponsored H-1B before

Management role

No

Posting history
  1. Mar 29, 2026 First posted
  2. Mar 29, 2026 Live now

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