Machine Learning Engineer,E-commerce Governance Algorithms Seattle Regular Job Seattle area,Washington USA,IT/Tech

Machine Learning Engineer, E-commerce Governance Algorithms

Location:

Seattle

Team:

Technology

Employment Type:

Regular

Job Code:

A04470

Responsibilities About the Team

Shaping the Future of Global E-Commerce Experience We are the Governance & Experience Algorithm Team — a trailblazing AI force at the forefront of building a trusted, efficient e-commerce ecosystem. Established in 2020, we leverage Graph Neural Networks, Multi-Objective Optimization, Time Series Prediction, and Large Language Models (LLM) to tackle some of the most complex challenges in e-commerce.

Quality Revolution Combat low-quality products and fraudulent practices across millions of listings

Detect low-quality items, counterfeit goods, and false advertising.
Identify "not-as-described" discrepancies between product claims and actual quality.
Flag risky sellers through comprehensive quality assessments.

Fulfillment & Logistics Experience Reinvention

Resolve delivery delays and non-receipt issues.
Prevent inventory shortages via predictive analytics.
Optimize warehouse operations for faster order fulfillment.

Why Our Work Matters

We protect users globally, ensuring safe shopping experiences in international markets and beyond.
Our innovations power Tik Tok’s mission to “Inspire Creativity, Bring Joy” by fostering trust and delight in every transaction.
We’re not just solving problems—we’re redefining industry standards for e-commerce product, service, and logistics experiences.

What You’ll Do

Lead Cutting
- Edge AI Projects

Build graph-powered risk networks to uncover similar product clusters and high-risk seller/creator groups in emerging markets, reducing false advertising incidents in global regions.
Develop LLM-based multi-modal systems using cross-modal fusion (text, images, behavioral data) to detect false advertising and low-quality products. Deploy AI-powered product business suggestions to improve seller compliance and user trust.
Lead time-series forecasting innovation for logistics performance metrics (e.g., delivery delays, cancellation rates), driving improvement in the e-commerce experience through predictive analytics.

Solve Complex Business Challenges

Develop multi-task models using MMoE and dynamic loss functions, driving measurable improvements in platform product, service, and logistics health.
Transform supply chains by optimizing warehouse product quality qualification strategies to cut missing recalls and reduce warehouse costs.
Unleash RPD/RPR growth through causal inference frameworks, identifying hidden correlations between CCR and user behavior to design personalized recommendations.

Redefine Industry Standards

Optimize LLM reasoning with DPO/GRPO to enhance fraud detection, outperforming traditional SFT methods.
Enhance tabular data modeling for better explainability in e-commerce risk assessments.
Construct heterogeneous graphs modeling product - seller - creator - video - user relationships, enabling improvements in product lifecycle insights and detection of similar product patterns.
Build unified time-series forecasting models across products, sellers, creators, videos, and users, achieving SOTA performance in predicting inventory shortages and demand surges.

Qualifications

Minimum Qualifications

Technical Mastery:

- Proficient in Python/C++ and machine learning frameworks (PyTorch/Tensor Flow).
Deep experience with graph neural networks, time series analysis, or LLM.

Domain Expertise:

- 3+ years in anti-fraud, prediction/forecasting, e-commerce governance, or related fields.
Track record of delivering AI solutions with measurable business impact.

Preferred Qualifications Problem
- Solving Mindset:

Passion for tackling ambiguous challenges and translating ideas into scalable solutions.
Strong communication skills to collaborate…

Machine Learning Engineer, E-commerce Governance Algorithms Seattle Regular