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Bryan Schaefer

@bryanschaefer

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Lead AI Engineer specializing in production ML, MLOps, and scalable healthcare analytics.

United States

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What I'm looking for

I seek senior technical roles building production ML and MLOps systems at mission-driven companies, focusing on scalable pipelines, model governance, and mentoring engineering teams.

I am a results-driven Lead AI Engineer with nine years of experience designing and deploying production machine learning systems across healthcare and technology platforms. I specialize in scalable ML pipelines, distributed training, and MLOps using Python, PyTorch, Spark, Kubernetes, and cloud platforms to support large-scale datasets and real-world evidence workflows.

I have led end-to-end AI lifecycle efforts that improved clinical outcome prediction accuracy by 23% on oncology data and accelerated data processing and validation pipelines by 40–60%. I mentor engineers, deploy inference services via containerized microservices, and establish monitoring and governance to reduce model-drift incidents in production.

Experience

Work history, roles, and key accomplishments

Current

Lead AI Engineer

Current

Flatiron Health

Sep 2021 - Present (4 years 6 months)

Directed design and deployment of production AI systems analyzing 12M oncology records, improving clinical outcome prediction accuracy by 23% and accelerating data processing by 40% through scalable pipelines.

Python PyTorch Apache Spark Airflow MLFlow Kubernetes AWS MLOps

Senior AI/ML Engineer

Flare

Dec 2018 - Aug 2021 (2 years 8 months)

Designed predictive AI systems on 8M signals to improve fraud and risk detection accuracy by 22% and reduced training cycles 40% via distributed GPU training and scalable experimentation pipelines.

Python PyTorch XGBoost Apache Spark MLFlow Distributed Training AWS Kubernetes

AI/ML Engineer

Uber

Feb 2018 - Oct 2018 (8 months)

Developed distributed ML pipelines processing 100M mobility events, improving demand forecasting precision by 12% and reducing prediction error by 8% using Spark and XGBoost.

Apache Spark XGBoost PyTorch scikit learn AWS Distributed Systems Experimentation

AI Intern

MindEase

Jan 2017 - Jan 2018 (1 year)

Analyzed 400K anonymized behavioral records and implemented classification models that improved baseline accuracy by 13% through feature engineering and cross-validation.

Python Pandas scikit learn Cross Validation Matplotlib

Education

Degrees, certifications, and relevant coursework

Texas Tech University

Bachelor of Science, Computer Science

2013 - 2016

Grade: 3.8

Activities and societies: Relevant coursework: Algorithms, Data Structures, Machine Learning, Operating Systems, Database Systems, Software Engineering; capstone projects analyzing 10K record datasets; implemented ML models using Python and Scikit-learn.

Completed a Bachelor of Science in Computer Science with coursework in algorithms, data structures, machine learning, and systems, and completed capstone projects involving data preprocessing, model training, and evaluation.