Anderson Junsu Park

Anderson Junsu Park

Product Manager & MLops Engineer @ SolverX

About

Product Manager & MLOps Engineer at SolverX We are solving hardest problems at the intersection of physics, AI, and manufacturing. My journey started in physics research at UC Berkeley and Lawrence Berkeley National Lab, where I worked on quantum computer hardware design with trapped-ion systems. There, I faced a fundamental bottleneck head-on: CAE simulations took over 100 hours, and this wasn't just a technical inconvenience—it was limiting the entire pace of innovation in hardware design. Those countless hours waiting for thermal and structural analysis results made me realize something critical: the manufacturing and design cycle is held back by a process that hasn't evolved with modern computing. Engineers and companies are still relying on CPU-based solvers and manual workflows that belonged to a different era. That's why I joined SolverX. We're building Physics Foundation Models—AI systems trained on real PDE data from manufacturing workflows. Our mission is to break the CAE bottleneck by making simulations run in seconds instead of hours, automating manual preprocessing, and expanding what's possible in design optimization. At SolverX, I focus on bringing this technology into the hands of engineers and manufacturers who are ready to move faster, iterate better, and innovate at a new scale. If you're interested in how AI and physics can reshape manufacturing, or if you're working on similar problems, let's connect. See https://www.solverx.ai/ for the most recent information.

Country

United States

City

Berkeley

Industry

Research

Skill

Python (Programming Language), RStudio, C++, qutip, python, R, LaTeX, Data Science, Research, Jupyter, NumPy, Quantum Computing, Pandas (Software), Machine Learning Algorithms, Quantum Information, Optimization

Experience

SolverX

Product Manager & MLops Engineer

SolverX

LinkedIn
2025 - Present · 1 yr

Building Physics Foundation Models that transform CAE workflows in global manufacturing Bridging the gap between cutting-edge AI research and real engineering challenges Leveraging my background in physics research and quantum computing to guide product strategy

University of California, Berkeley

Undergraduate Research Assistant

University of California, Berkeley

LinkedIn
2022-8 - 2024-12 · 2 yrs 5 mos

Berkeley, California, United States

Researched with Professor Hartmut Haffner and Ion Trap Group

Berkeley Lab

Lab Assistant

Berkeley Lab

LinkedIn
2022-10 - 2023-4 · 7 mos

Berkeley, California, United States

Researched with Professor Barbara Jacak and Dr. Nicole Apadula on developing cooling strategies for the silicon tracking detector for the upcoming Electron-Ion Collider (EIC).

Queti

Undergraduate Researcher

Queti

2022-5 - 2022-7 · 3 mos

Suwon

Researched with Prof. Junki Kim at Quantum Engineering with Trapped Ions (QuETI) on building a trapped-ion hamiltonian simulator for simpified spin-boson system. ◦ Developed a Molmer-Sorensen Gate simulator which gives the system’s state as density matrix at given time and tracks the vibrational quantum number of the qubit state using QuTIP.

2nd Infantry Division - Korea

Korean Augmentation To the United States Army

2nd Infantry Division - Korea

LinkedIn
2020-9 - 2022-3 · 1 yr 7 mos

Camp Casey, South Korea

My role in the army was Medic Non-Commissioned Officer in 2nd Infantry Division 210 Brigade 6-37 Battalion HHB Battery. I have worked to maintain the health status of the soldiers in 6-37 Battalion and performed leadership to junior soldiers and taught immediate medical treatment methods in case of emergency.

University of California, Berkeley

Undergraduate Student Researcher

University of California, Berkeley

LinkedIn
2020-2 - 2020-9 · 8 mos

Berkeley, California, United States

I have researched in the neutrino oscillation potential in simulation of the full isotropic Quantum Kinetic Equations in conditions relevant to core-collapse supernovae. My role was creating automated test cases using C++ and Mathematica, Implementing GSL-integrator to increase the accuracy of full isotropic Quantum Kinetic Equations by calculating neutrino energy and entropy transfer in core-collapse supernovae, using Savio high performance computer to simulate the core-collapse supernovae, and writing graph codes in python that takes data from simulation and visualize energy and entropy change.

Education

University of California, Berkeley

University of California, Berkeley

LinkedIn

Physics

2019-8 - 2024-12 · 5 yrs 5 mos
University of California, Berkeley

University of California, Berkeley

LinkedIn

Data Science

2020-8 - 2024-12 · 4 yrs 5 mos

Anderson Junsu Park's Contact Information

Email

******@***.com

Phone

(**) *** ****

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