Daniel Karn

Designed and deployed large-scale ML models in production, training ranking and recommendation systems in PyTorch and TensorFlow that improved match quality metrics by 24% across 10 million users. Led end-to-end model development, feature engineering, and online A/B testing for a two-tower recommendation model, lifting click-through rate by 18% and conversion by 11% in production.

Deployed impactful machine learning solutions to large-scale production systems, training deep learning models in PyTorch and TensorFlow on 500 million labeled samples with Apache Spark. Built recommendation systems and ranking models for retail clients in Python with two-tower embeddings and learning to rank, increasing recommendation click-through rate by 18% in A/B tests.

Developed core features of the Databricks platform in Python, Scala, and SQL, enabling large-scale production machine learning for more than 2,000 enterprise customers worldwide. Built MLflow tooling for experiment tracking, model registry, and model serving, supporting deep learning workloads in PyTorch and TensorFlow for data science teams on AWS and Azure.

Developed large-scale production systems on Azure in C#, Python, and SQL for consumer and enterprise products, serving 5 million monthly active users with 99.9% availability. Built machine learning features for recommendation systems using Azure ML and TensorFlow, improving content recommendation click-through rates by 15% in online A/B tests.

Developed data processing features in Python and SQL for an internal analytics platform, supporting reporting workflows used by 300 engineers across multiple product teams. Built automated test suites and CI pipelines for cloud services on Azure, increasing code coverage from 60% to 85% and catching regressions before each release.

Bachelor's Degree in Computer Science from the University of Central Florida, completed in 2012.