Richard Yang

Senior AI Research Engineer with 10 years of experience building machine learning systems, LLM pipelines, and model training infrastructure for production AI applications. Specializes in LLM fine-tuning, PyTorch, distributed training, and AI architecture with ownership of end-to-end research engineering workflows. Led technical projects, mentored engineers, and delivered model systems improving automated decision workflows across large-scale applications.

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.

Master's Degree - Computer Science - Stanford University (2017 - 2019)

Bachelor's Degree - Computer Science - University of Wyoming (2012 - 2017)