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Machine Learning Research Software Engineer
- Solid understanding of machine-learning, physics, and strong analytical problem-solving skills - Extensive experience using machine learning algorithms (random forest, xgboost, CNN, etc) for classification, NLP. - Extensive experience with building/integrating machine learning models for large scale inline data processing - Familiar with LLM, qLoRA, prompt engineering for GenAI tasks. - Experienced with distributed large-scale simulation, data mining/analysis, and visualization - Excellent communication skills and thrive in collaborative settings - Multi-lingual with computer/scripting languages, English, Mandarin, Cantonese Specialties: Expert language: Python, Exposed to: C/C++, Shell Scripts, PowerShell, Fortran Software packages: TensorFlow, Pandas, NumPy, SciPy, Scikit-Learn, hg/Git, OpenCV, Matlab, Enzo, yt
UCSD
Doctor of Philosophy (Ph.D.), Physics with specialization in Computational Science
January 1, 2005 – January 1, 2013
UC San Diego
Bachelor’s Degree, Physics with specialization in Astrophysics
January 1, 2000 – January 1, 2005
Palo Alto Networks
Principal Machine Learning Engineer
September 1, 2025 – Present
Hybrid
Broadcom
Principal Machine Learning Research Engineer
January 1, 2018 – September 1, 2024
San Francisco Bay Area
SFL Scientific
Machine Learning Engineer
April 1, 2017 – January 1, 2018
Portland, Oregon Metropolitan Area
Intel
Machine Learning Engineer (Inline Analytics)
September 1, 2014 – May 1, 2016
Portland, Oregon
UC San Diego
Postdoctoral Researcher
January 1, 2014 – August 1, 2014
University of California, San Diego, Super Computer Center, La Jolla, California
University of California, San Diego
Graduate Student Researcher
March 1, 2007 – December 1, 2013
La Jolla, California
Cultural Fit Analysis
The candidate has a strong background in Machine Learning Engineering across various industries (cybersecurity, consulting, semiconductor manufacturing) and academic research. While the target role is 'Data Analyst', the candidate's experience is heavily skewed towards Machine Learning Engineering and Research. This indicates a potential mismatch in the specific day-to-day responsibilities and focus, as a Data Analyst role typically emphasizes data visualization, reporting, and business intelligence, rather than ML model development and deployment. The breadth of skills is high within ML/data science, but less so in traditional data analysis tools and methodologies. The project diversity is good within the ML domain.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight collaboration, coordination of projects, and training others, suggesting strong interpersonal and leadership skills. The academic background and research contributions indicate a methodical and problem-solving approach. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.