Tristan Mckinney
Applied Scientist III at Amazon
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTristan Mckinney is an Applied Scientist III at Amazon in San Francisco with nine years of experience bridging theoretical physics and machine learning for product-focused computer vision and HCI work. He earned a PhD in theoretical physics from Caltech studying effective field theory and Fermi-surface phenomena, and has translated that rigorous mathematical background into practical ML solutions for human health, portfolio allocation, and large-scale vision systems. At AWS and Amazon he has shipped tutorials and tooling—contributing notable SageMaker Ground Truth examples—to help teams label, train, and deploy object-detection models. Known for pairing deep analytical modeling with hands-on engineering, he excels at taking research prototypes into production-facing features. Colleagues value his blend of academic rigor, practical ML engineering, and clear technical communication.
9 years of coding experience
6 years of employment as a software developer
B. S. Engineering Physics, B. S. Engineering Physics at University of California, BerkeleyCalifornia Institute of TechnologySouth Carolina Governor's School for Science & Mathematics
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
amazon-sagemaker10
jupyter-notebook10
computer-vision10
machine-learning10
data-science9
python9
aws9
deep-learning8
deeplearning-ai8
object-detection8
trainings7
inference7
mlops7
Programming languages (4)
Github contributions (5)
aws/amazon-sagemaker-examples
Jan 2019 - Sep 2020
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:7 reviews, 10 commits, 8 PRs in 1 year 8 months
Contributions summary:Tristan primarily contributed to a Jupyter Notebook tutorial demonstrating how to use Amazon SageMaker Ground Truth for object detection, covering data preparation, labeling job setup, result analysis, model training, and deployment. They implemented code for processing, analyzing, and visualizing the results from a Ground Truth labeling job. The commits involved significant modifications to the Jupyter Notebook, including adding code, correcting links, and embedding images.
pythonjupyter-notebooktrainingawssagemaker
natsirtguy/flykey
Mar 2017 - May 2017
Contributions:28 commits, 13 pushes, 1 branch in 1 month
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.