Junze Liu

Critic
DISC Type : C

Applied Scientist at Amazon

Mountain View, California, United States

Overview

Junze Liu is an Applied Scientist on Amazons Rufus team, where he applies large language models (LLMs) to enhance customer experiences. He earned his Ph. D. in Computer Science from the University of California, Irvine, specializing in deep learning, computer vision, and causality, with a focus on physics and bio-imaging applications.


Junze holds a patent for a utility model of an anti-scald soldering iron, which features a protective casing and a unique sliding chute mechanism.

Personality Overview

Precise

Objective Thinker

Negotiator

They don’t appreciate bells and whistles unless backed by data.  They like to do things independently and don’t look for support from others. They choose to analyze logically and value facts to emotions.

Topics They Care About

LLMs in E-commerce
His current role at Amazon involves leveraging LLMs to innovate and improve customer-facing solutions and experiences.
Deep Learning for Physics
His Ph. D. research and subsequent presentations focused on using deep learning for neutrino reconstruction and generative models for calorimeter simulation.
AI in Medical Imaging
He has co-authored papers and given talks on applying deep learning for bioimaging, including tracking vitreoretinal surgical instruments.

Media Appearances

Junze has no verified media appearances

Work History

1-2025
Applied Scientist at Amazon
6-2020 - 12-2024
Graduate Student Researcher at UC Irvine
1-2020 - 5-2020
Teaching Assistant at UC Irvine
9-2019 - 12-2019
Reader at UC Irvine
6-2024 - 9-2024
Applied Scientist Intern at Amazon

Education

2019 - 2024
Doctor of Philosophy - PhD from UC Irvine
2016 - 2018
Master of Engineering (M.Eng.) from University of Illinois Urbana-Champaign

More Information

Social Presence :

Prographics :

Exp : 5 Location : Mountain View, California, United States Job Level : N/A Designation : Applied Scientist at Amazon
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Insights For Selling To Junze

During A Call Or A Meeting

DO's

  • Use phrases like ‘expect X% improvement’, ‘data clearly shows’ etc.
  • Be formal and objective, they will appreciate it more
  • If you can, show them industry reports or analyst comments instead of sharing anecdotal stories

DONT's

  • Do not use very emotional or colorful language
  • Don’t try too hard to build a relationship with them
  • Avoid pushing them too much to involve other stakeholders unless it is critical

When Cold Calling

When Writing An Email

While Negotiating & Closing

    The secret to closing fast with Junze is

  • Proven ROI, pricing and objective proof points are the factors that sway their decision.
  • Will you ever get a clear answer from Junze

  • They are comfortable saying no if they are convinced that it is the correct decision.

Insights For Deal Planning

    How fast (or slow) will Junze move?

  • They are neither the fastest nor the slowest decision makers, they are somewhere in the middle.
  • Can Junze take some risk or not?

  • They can bear some risk if their analysis backs the decision.

You And Junze

Personality Compatibility


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