Miray Yuce is a Senior Machine Learning Engineer based in San Francisco with over a decade in software engineering and 6+ years focused on production ML systems, specializing in scalable infrastructure like feature stores, batch inference frameworks, and distributed dataloaders. She has driven large-scale ML platforms at Adobe and Twitter—building a self-serve batch inference system that scaled to 2,000 GPUs and processed 1.5 billion images in 48 hours, and designing Twitter’s first fully managed feature store with end-to-end offline/online pipelines. Miray blends deep distributed-systems experience from enterprise middleware to modern ML platforms, delivering reliable, observable systems that accelerate research and training across billions of records. She’s pragmatic about tooling: authored a PyArrow-based Parquet manager that cut dataset operations from days to hours and standardized workflows across teams. Her background in active learning and NAS from graduate research surfaces in efficient data and model strategies, and she routinely validates outputs with creative methods such as LLMs-as-judges to maintain data quality at scale.
8 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at The University of Freiburg
Bachelor of Science (B.Sc.), Computer Engineering, Bachelor of Science (B.Sc.), Computer Engineering at Galatasaray University
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