I'm an experienced ML Scientist/Engineer passionate about combining deep theoretical knowledge with practical coding skills to build scalable and impactful solutions.

- Machine Learning Development: Proficient in developing, training, and fine-tuning models for various applications, including Natural Language Processing (NLP), Computer Vision (CV), and Bioinformatics. - Large Language Models (LLMs): Extensive experience in fine-tuning LLMs for enhanced task-specific performance. - Statistical Modeling & Visualization: Skilled in statistical analysis and using visualization tools to uncover hidden insights and inform decision-making.

Senior Platform Engineer at Quantiphi, an AI-first digital engineering company. Passionate about building scalable AI/ML platforms and cloud-native infrastructure. Based in the San Francisco Bay Area, I enjoy connecting with fellow AI tinkerers to explore the latest in machine learning, LLMs, and intelligent systems.

Large language models, agentic AI systems, AI infrastructure at scale, emerging trends in generative AI, and applied ML use cases across enterprise and product development.

I am a Computer Science graduate student at San Jose State University with a strong foundation in software engineering, cloud computing, and AI-driven solutions. With over three years of industry experience at UBS and Credit Suisse, I have designed and deployed scalable, distributed systems and improved system efficiency through performance optimization and automation. My passion lies in building intelligent, high-performance applications that combine cloud infrastructure, data-driven insights, and secure system design to solve real-world problems.

I am deeply interested in artificial intelligence, distributed systems, and cloud-native application development. I enjoy exploring how AI agents, automation, and scalable backend architectures can work together to build intelligent, context-aware systems. I’m particularly curious about context engineering, low-latency data retrieval, and autonomous decision-making agents that bridge real-world actions with digital intelligence.

Scarlett Zhang is a Software Engineer at Adobe based in San Francisco, California. With two years of professional experience, she has developed expertise in software development with a focus on AI technologies. Scarlett’s educational background spans several prestigious institutions, including Carnegie Mellon University School of Computer Science, UC Irvine, and the Technical University of Munich, where she studied software engineering, computer engineering, computer science, and informatics. Her diverse academic and professional experiences contribute to her strong foundation in developing innovative software solutions within the tech industry.

Context Engineering

Currently, Scarlett Zhang is working on scalable system development at Adobe. Our team is making efforts to understand and experiments on Mastra framework