# Ryan Cheng > Staff ML Engineer @ Meta | ex-AWS Location: San Francisco, California, United States Profile: https://flows.cv/ryancheng Ads Machine Learning at Facebook Previously: Applied Machine Learning, Amazon ML Solutions Lab ## Work Experience ### Staff Software Engineer, Machine Learning @ Meta Jan 2021 – Present | San Francisco, California, United States Scaled FB Reels Overlay Ads from 0 to $1 B/year through Ads Personalization ### Sr. Deep Learning Architect @ Amazon Web Services (AWS) Jan 2019 – Jan 2021 Responsible for building ML solutions and designing systems to deploy them. Built menu digitization system by stacking Amazon Textract with custom Object Detection and Text Classification models. System is being used in production to add advanced search functionality to a restaurant aggregation and food delivery platform. Developed statistical technique to predict winners of a tournament-style autonomous racing league. Designed architecture to serve results to commentators in real time. Publication: https://aws.amazon.com/blogs/machine-learning/delivering-real-time-racing-analytics-using-machine-learning/ Implemented architecture to calculate Formula 1 driver rankings. Publication: https://aws.amazon.com/blogs/machine-learning/the-fastest-driver-in-formula-1/ ### Sr. Data Scientist @ 2nd Order Solutions Jan 2017 – Jan 2019 | Washington, District Of Columbia Financial services consulting. Reduced operating expenses of major US credit card issuer by $81 MM annually. Built machine learning models to: 1. reduce losses with an improved algorithmic credit policy, and 2. optimize contact strategy on delinquent accounts. Navigated stringent regulatory landscape to ensure models were legally compliant. Created account and portfolio-level valuation forecasts by integrating machine learning predictions with data on customer behavior, seasonality, and macro-economic trends. Improved runtime of client’s daily reporting code from 12 hours to 60 seconds. Leveraged optimized data structures and array programming in Python to develop solution within client’s production environment. Designed company-wide training sessions and initialized Pyspark codebase for performing distributed data wrangling and model training on AWS EMR/Sagemaker. Represented firm at AWS re:Invent 2018. ### Machine Learning Research (Genetics) @ University of Virginia Jan 2013 – Jan 2017 | Charlottesville, Virginia Identified 5 high-confidence candidate mutations for coronary artery disease using machine learning. Accelerated pathology research by providing alternative to wet lab intensive workload of studying mutations correlated with complex diseases. Trained support vector machines on genomic data to identify harmful mutations. Deployed models on SLURM cluster to achieve scalability when performing tissue-specific inference on genome-scale datasets. ### Machine Learning Research (Computational Biology) @ University of Virginia Jan 2013 – Jan 2017 Developed novel approach towards personalized medicine by creating a tool to study the impact of bacterial communities within the body (microbiome) on human health. Outperformed status quo technique (Flux Balance Analysis) on benchmark dataset by ~20%. Implemented Skip-gram in Tensorflow to create vector representations of bacterial metabolic network reconstructions, method was generalizable to bacterial communities. Used constraints based metabolic reconstruction models to investigate the relationship between P. aeruginosa metabolic phenotype and pathogenicity/environmental niche. ### Data Science Intern @ Commonwealth Computer Research, Inc. (CCRi) Jan 2016 – Jan 2016 | Charlottesville, Virginia Engineered vector representations of social networks using unsupervised machine learning algorithms. Used the representations to infer group membership. Developed a scalable algorithm to calculate the field of view from any point on Earth. ### Datafest Hackathon 1st Place @ American Statistical Association - ASA Jan 2016 – Jan 2016 | Charlottesville, Virginia Area Awarded 1st place overall and 2nd place in “Best Use of External Data”. Created linear regression model to optimize Ticketmaster’s concert pricing. Geocoded addresses to determine distance traveled by concert attendees. Used distance traveled as a proxy for fan devotion to characterize the elasticity of demand for artists as a function of venue demographics, segmented by artist genre. ## Education ### Bachelor’s Degree with Highest Distinction in Biomedical Engineering (B.S.) University of Virginia ### BA with Highest Distinction in Physics University of Virginia ## Contact & Social - LinkedIn: https://linkedin.com/in/ryan-cheng-092b14104 --- Source: https://flows.cv/ryancheng JSON Resume: https://flows.cv/ryancheng/resume.json Last updated: 2026-04-12