Chris Rawles
Staff Research Engineer at Google DeepMind
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolChris Rawles is a Staff Research Engineer at Google DeepMind with 12 years of experience building and deploying machine learning systems and data-driven solutions. He moved through roles at Google from ML Solutions Engineer to Senior Software Engineer before joining DeepMind, blending product-focused ML engineering with research-level rigor. His background in geophysics (MS, UC Berkeley BA) informs a strong analytical mindset and experience applying advanced modeling to noisy, real-world signals. An active contributor to Google Cloud training labs, he has hands-on experience optimizing models (e.g., batch normalization for MNIST) and running experiments on Cloud ML Engine. Based in New York, he focuses on elegant, creative technical solutions that bridge research prototypes and production-grade deployments.
12 years of coding experience
11 years of employment as a software developer
B.A Geophysics, B.A Geophysics at University of California, BerkeleyM.S Geophysics, M.S Geophysics at University of Wisconsin-Madison
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
keras10
machine-learning10
tensorflow10
batch-normalization10
python10
gcp9
gcloud9
google-cloud-platform9
google-cloud-ml-engine9
image-classification8
Programming languages (8)
Github contributions (5)
GoogleCloudPlatform/training-data-analyst
Feb 2018 - Mar 2020
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
Role in this project:
Data Scientist
Data ScientistContributions:33 commits, 30 PRs, 41 pushes in 2 years 1 month
Contributions summary:Chris's commits primarily focus on implementing code related to batch normalization within a machine learning context, specifically for an MNIST classifier. This indicates an involvement in model development and optimization. The user is also working on Cloud ML Engine, running experiments, and potentially deploying the model, suggesting experience with the Google Cloud Platform.
gcpgoogle-cloud-platformgoogle-cloudtraining
crawles/text-analytics-service-example
Aug 2016 - May 2017
Deploy sentiment analysis using Flask
Contributions:86 commits, 81 pushes, 1 branch in 9 months
pythonflask-apideploymentflasksentiment
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