Machine Learning Engineer Graduate; E-Commerce Supply Chain & Logistics PhD
Job in
San Jose, Santa Clara County, California, 95199, USA
Listing for:
Lucie Richardson AB
Full Time
position
Listed on 2026-07-13
Job specializations:
-
Software Development
Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 120000 - 150000 USD Yearly
USD
120000.00
150000.00
YEAR
Job Description & How to Apply Below
Position: Machine Learning Engineer Graduate (E-Commerce Supply Chain & Logistics) - 2026 Start (PhD)
Technology
Machine Learning Engineer Graduate (E‑Commerce Supply Chain & Logistics) - 2026 Start (PhD)
Location:
San Jose
Employment Type:
Regular
Job Code: A245110
Team Introduction
Join the E‑commerce Global Supply Chain and Logistics team at Tik Tok! We're enhancing the shopping experience and reducing logistics operational costs. We are seeking brilliant and motivated graduate software engineers, who are eager to apply their knowledge in machine learning (ML), operations research (OR), data mining, and statistical inference to real‑world challenges.
We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.
Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.
Responsibilities
Responsible for the development of deep learning and operations research models and related intelligent systems for the supply chain and logistics of the global E‑Commerce business.Utilize e‑commerce big data and deep learning models to predict end‑to‑end estimated time of arrival (ETA), and some logistics events such as failed delivery, delivered but not received to enhance the user logistics experience. Build logistics network knowledge graphs and predict the spatio‑temporal trajectory sequence of express packages through deep learning, statistical inference and other algorithmic methods. Use NLP and LLM algorithms to handle address problems such as address verification and address suggestion.Utilize time series forecasting techniques to predict sales at different granularities and horizons, such as warehouse‑level manpower forecast in#J-18808-Ljbffr
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