San Francisco, California, United States
My expertise lies in building and designing reliable, scalable software to support ML products. I've had professional experience as a Machine Learning Engineer, Full Stack Software Engineer, and Product Manager. If you want to talk more about anything, feel free to book a chat with me! On the Artificial Intelligence side, I'm knowledgable in Search and information retrieval (IR) systems including: ranking and relevance, query understanding and intent, semantic embeddings, and recommendation. I've built infrastructure and ingestion pipelines to support these products. I've worked with: Natural Language Processing (NLP), Large Language Models (LLMs), Fine-tuning, post training, Generative Modeling, and deploying models. I hope to learn more about the tech industry and to apply my leadership, interpersonal, and organizational skills in a real-world setting. Always eager to learn and striving to improve. I've had Full Stack developer with experience building web and mobile applications. I specialize in JavaScript, working with React, Ruby on Rails, Node.js, Express.js, REST APIs, HTML, and CSS. I also have experience working with Java, C++, PostgreSQL, and Git. I've also had PM experience at a small startup, leading features from design to launch in 4 months. I did market research, user interviews, competitive analysis, road mapping, backlog prioritization, and make data driven decisions using decision making frameworks. Take a look at my work and I hope to get in touch! Calendly: https://calendly.com/amanda-yu-1/chat [email protected] My website: https://amanda-yu-website-git-main-amandayu.vercel.app/ Github: github.com/amandayu1 Plum Profile: https://secure.plum.io/p/Ri0tWzrtGLN3Mb9XsIfyYQ
Search Platform Team Skills: Deployment, Docker, Kubernetes, Kafka, Infrastructure
● Analyzing synthetic dataset generation methods (GANs) to reduce racial bias in facial recognition models ● Presented published paper at IEEE Ethics Conference, June 2025 ● Presented research as a keynote speaker at ELO Conference, 18 January 2024 ● Bias in Image Analysis Professor Lai-Tze Fan, in the U&AI Lab, at the University of Waterloo Part of the Games Institute
AWS Auto Scaling, EC2 Product, Predictive Scaling team ● built end to end simulator for ML models forecasting demands, comparing inference outputs
Accuracy Framework/ Dashboard Team Skills: Anomaly detection, Change point detection (CPD), Hyperparameter tuning, Segmentation, Demand Forecasting, Product Prediction, Timeseries data model analysis, Edge detection, Event detection Tools: Pytorch, PySpark, Azure, Databricks, Numpy, Pandas