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Principal Machine Learning Engineer

Nomic AI

Leading the Embedding Team and implementing state-of-the-art embedding models.

• Implemented Nomic Embed, powering Atlas embedding space visualization for text and multimodal applications
• Trained and deployed Nomic Embed Text, outperforming OpenAI with 15M+ monthly downloads
• Trained Nomic Embed Vision, the first multimodal embedding space performing highly on both multimodal and text embedding benchmarks
• Deployed Nomic Embed models serving as backbone to Atlas, handling billions of tokens monthly

Machine Learning Engineer

Deep Genomics

Focused on genomics and RNA research using machine learning techniques.

• Reimplemented and improved DeepMind gene expression model in Tensorflow by 15-30% on TPUs
• Led research on representation learning for chemical modifications of RNA molecules using Transformer based architectures
• Helped standardize and run highly distributed training of attention-based models across many GPUs and TPUs
• Reduced latency of large Deep Learning models by ~16x utilizing model distillation and TensorRT
• Ported Maximal Update Parameterization to Tensorflow for zero-shot hyperparameter transfer across model width and depth

Machine Learning Engineer

Amazon

Worked on Alexa Speech Speaker ID model and optimized AWS infrastructure.

• Maintained and improved evaluation pipelines for Alexa Speech Speaker ID model for 25M+ global consumers
• Improved runtime of large PySpark processing jobs by 25% and reduced job failures by 50%
• Drove redesign and engineering for Alexa SpeakerID weekly evaluation pipeline, reducing manual work by 75%
• Optimized AWS EMR clusters for ML Ad Ranking Team, reducing operational costs by 50% leveraging AWS Lambda and AWS Event Bridge

Bachelor of Science

Davidson College

Computer Science