Michael Schock Email & Phone Number

Seattle, Washington, US

• Spearheaded orchestration and automation of AI agent training (with each agent an ensemble of PyTorch models acting as a mixture of experts) into an MLOps pipeline backed by a self-hosted in-cluster duo of Prefect.
• Rapidly prototyped a working MVP showcasing how we could easily scale the training runs via the Prefect-Ray integration and an in-cluster or Anyscale Cluster, also presenting SkyPilot as a way to abstract Ray and cloud.
• Modernized the developer experience for the AI Platform team by bringing in Tilt to watch for changes in the Kubernetes manifests for full Docker build/pushes, thereafter updating pods without reload for fast.

Atlanta, Georgia, US

• Served as a Teaching Assistant (TA) for CS 7639: Cyber-Physical Systems Design & Analysis.

Fremont, California, US

• Created a proof of concept (POC) for a data engineering pipeline to extract, transform, and load images and their corresponding labels from various data sources and formats into the COCO dataset format with k-fold.
• Constructed a POC for a data modeling pipeline to train and validate a PyTorch Faster R-CNN model with various modifications for computer vision tasks such as object detection and image segmentation from a.
• Assembled a POC for a model deployment pipeline to deploy a model produced by the data modeling pipeline into a local docker container running TorchServe (or SageMaker) to run inference tests upon that model and to.
• Designed a dashboard using Amazon QuickSite to automatically generate visualizations, including emails pointing to those visualizations, that displayed the performance of the served model and assigned SageMaker.
• Built a POC framework using Kedro and DVC to join the data engineering, data modeling, and model deployment pipelines, running pipeline components only when artifacts tracked by DVC changed.

San Mateo, California, US

San Mateo, California, US

• Refactored the data ingestion pipeline into more modular components.
• Drove the model explainability implementation.
• Led the development of the forecasting service.
• Built a worker service that operated on graph structures representing machine learning tasks and their states.
• Developed various features and addressed bugs in our systems.

Austin, Texas, US

• Engineered features for the BigCommerce storefront platform.
• Wrote unit tests for all new and changed code, increasing code coverage.
• While taking the lead on building out a new feature, discovered an opportunity to improve the codebase by refactoring the code into an easier-to-reason-about structure such that future additions wouldn't require as.

San Francisco, CA, US

• Implemented features for the Customer Enterprise Portal for Autodesk.
• Caught up to speed quickly, diving into the codebase with minimal support.
• Formed a POC to re-architect a centerpiece of the Portal, refactoring spaghetti code and building a more well-organized system that can easily accommodate new types of Autodesk products and services along with their.
• Taught and led other developers with patience and a desire to improve their understanding.
• Responded to bugs, defects, and applicable business concerns with a strong sense of urgency.

San Francisco, CA, US

• Client of Globant.

Dublin, California, US

• Converted features from the legacy implementation of PlantLog, which ran only on Windows as a native application, to a web and mobile application hosted in the cloud.
• Re-architected the backend to be RESTful, refactoring a single large switch case into resource-specific endpoint logic.
• Converted legacy reporting components for use in the new system.
• Rebuilt the mobile app, using the BackboneJS framework to organize the application.
• Added barcode scanning/lighting functionality to the mobile app.

Master Of Science - Ms, Computer Science, Specialization In Machine Learning

Bachelor Of Arts - Ba, Physics