Senior AI Research Engineer - Salesforce - San Francisco, CA
(2025-08)
- Built 6 production machine learning systems using PyTorch and Python to deliver AI capabilities across large-scale enterprise applications.
- Designed 8 end-to-end LLM training pipelines including data preparation, fine-tuning workflows, evaluation processes, and model deployment automation.
- Owned 4 AI research initiatives by implementing experimental architectures, analyzing model behavior, and mentoring engineers on machine learning development practices.
- Architected distributed training workflows using GPU-based infrastructure and improved model iteration cycles for research and production teams.
- Collaborated with 5 engineering groups to integrate machine learning models into reliable application workflows with measurable improvements in automation.
Machine Learning Engineer - Adobe - Los Angeles, CA
(2020-06 - 2025-07)
- Developed 7 deep learning models using PyTorch and TensorFlow for intelligent document, content, and automation workflows.
- Implemented 5 machine learning pipelines covering data processing, feature engineering, model training, and evaluation for production applications.
- Created model evaluation frameworks using Python that enabled researchers to compare experiments across multiple training approaches.
- Improved ML development workflows by introducing reproducible experiments, version-controlled datasets, and automated validation processes.
Software Engineer - Adobe - Los Angeles, CA
(2019-06 - 2020-05)
- Built 10 Python-based data processing tools supporting machine learning experimentation and large-scale application features.
- Implemented 6 backend services and APIs that provided reliable access to data and model-driven functionality.
- Improved system performance by redesigning 4 critical processing workflows and reducing engineering complexity.
AI Scientist Intern - NIO - San Jose, CA
(2018-06 - 2018-09)
- Developed 3 machine learning prototypes using Python and deep learning frameworks to analyze vehicle-related datasets.
- Evaluated 5 predictive modeling approaches and analyzed experimental results to improve research outcomes.
Research Assistant - University of Southern California - Los Angeles, CA
(2016-05 - 2017-08)
- Created 4 machine learning research tools using Python to process experimental datasets and evaluate algorithm performance.
- Published 2 technical analyses documenting model experiments, data processing methods, and research findings.
