E‑Commerce Risk ML Engineer

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

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) with company match

Paid parental leave

Short-term and long-term disability

Life insurance

Wellbeing benefits

Paid holidays and personal time

Job summary

TikTok Inc. seeks graduates to join the Governance and Experience Algorithm team in the United States. You will study and develop algorithms to detect risks and violations in e-commerce, and help build a scalable ecosystem for platform services.

Candidates should be ready to onboard by year-end with clear graduation timelines in their resume. The role involves identifying fraud patterns, optimizing large-scale libraries, and contributing to data construction and model alignment.

Qualifications

• Bachelor's degree in software-related field and recent completion.
• Solid coding in Linux, Python/Go/C++ with strong fundamentals.
• Experience in ML/DL theory and practical projects.

Responsibilities

• Identify algorithms for risk/violation issues in e-commerce.
• Mine and identify algorithms to detect fraudulent behavior.
• Optimize algorithms for large-scale libraries and content.
• Data construction, fine-tuning, CoT/LLM alignment in e-commerce.
• Explore reinforcement learning for intelligent dispatching/audit scheduling.

Skills

Python

Go

C++

Linux

Data structures

Algorithms

Machine learning

Deep learning

NLP

Computer vision

Multimodal

LLM

Education

Bachelor's degree in Software Development/CS/CE or related

Master's degree in Software Development/CS/CE or related

Job description

TikTok Inc. seeks graduates to join the Governance and Experience Algorithm team in the United States. You will study and develop algorithms to detect risks and violations in e-commerce, and help build a scalable ecosystem for platform services.

Candidates should be ready to onboard by year-end with clear graduation timelines in their resume. The role involves identifying fraud patterns, optimizing large-scale libraries, and contributing to data construction and model alignment.