Blake Byerly, Software Developer in Seattle, WA, United States
Blake Byerly

Software Developer in Seattle, WA, United States

Member since June 9, 2017
Blake possesses both startup and large enterprise experience leveraging machine learning (deep learning and classical techniques) to create value. He has applied machine learning to a variety of problems including network event correlation and incident forecasting, resource-constrained scheduling, and at-scale E-commerce applications.
Blake is now available for hire


  • Zulily
    Java, Spark, H20, Kubernetes
  • Boldiq
    C, C++, DLIB
  • Cisco Systems
    Python, NumPy, Pandas, SciPy, Matplotlib, SciKit-Learn, CVXOPT, Keras...



Seattle, WA, United States



Preferred Environment

Windows 10

The most amazing...

...skill I've developed was deep reinforcement learning for resource-constrained scheduling.


  • Machine Learning Engineer

    2018 - PRESENT
    • Adapted an API for deploying a scalable, cloud-based machine learning model (Go/Kubernetes/Docker).
    • Developed a driver for communicating with said API (Apache Airflow).
    • Wrote a back-end process for pulling data into an in-memory cache (Java/SQL).
    • Developed EDA and validation metrics (Java/H20).
    Technologies: Java, Spark, H20, Kubernetes
  • Machine Learning Engineer

    2018 - 2018
    • Developed an AI-based optimization engine employing deep reinforcement learning for learning strategies for optimized resource-constrained scheduling.
    • Maintained and debugged ‘Solver’, company’s proprietary real-time optimization engine (for private aviation scheduling).
    Technologies: C, C++, DLIB
  • Senior Data Scientist

    2016 - 2017
    Cisco Systems
    • Oversaw AI for optimizing network monitoring. Developed a machine learning pipeline allowing for analysis of Cisco’s unstructured data (through Splunk’s Rest API) using ensemble techniques from the SciKit-Learn library. Initiative resulted in improved event correlations on Cisco CMS’s network management platform.
    • Extended the initiative to perform network incident forecasting using deep learning techniques on a customized architecture (NLP, semantic analysis via CNNs) using a TensorFlow backend and Keras (high-level API).
    • Architected of “Splunk to Excel," an automated reporting mechanism.
    Technologies: Python, NumPy, Pandas, SciPy, Matplotlib, SciKit-Learn, CVXOPT, Keras, TensorFlow, Splunk
  • Intern/Research Assistant

    2015 - 2016
    Ecole Polytechnique Federale de Lausanne
    • Developed embedded DB and SDC-constrained scheduling software in Java for High-Level Synthesis and data-flow programming applications (with applications to embedded systems). Accepted into the doctoral program of the EPFL.
    Technologies: Cal, Java, Spring, lp_solve, MySQL
  • Intern/Research Assistant

    2014 - 2015
    • Developed insertion loss and cross-talk cable models for 4th-generation DSL standard, The use of vectoring to achieve higher data rates requires an accurate understanding of how the cable manipulates intended signaling. Models were used in the Broadband Forum for standardization purposes.
    Technologies: Matlab, Optimization toolkit, Signal processing toolkit, Transmission line theory, Oscilloscope


  • Languages

    Python, Bash, Java, C, C++, SQL, Golang
  • Libraries/APIs

    PySpark, Dlib, Keras, TensorFlow
  • Tools

    IntelliJ IDEA, BigQuery, Apache Airflow, Splunk, MATLAB, Google Kubernetes Engine (GKE)
  • Platforms

    Kubernetes, Docker, H20, Google Cloud Platform (GCP), Jupyter Notebook, Ubuntu 14.04, Visual Studio 2017, Windows
  • Storage

    Google Cloud Storage, SQL CE, Redis
  • Other

    Google BigQuery, Machine Learning, Optimization Algorithms, Natural Language Processing (NLP)
  • Frameworks

    Apache Spark
  • Paradigms

    Scrum, ITIL, Agile


  • Master's degree in Electrical Engineering and Information Technology
    2012 - 2015
    ETH-Zurich - Zurich, Switzerland
  • Bachelor's degree in Electrical Engineering
    2009 - 2012
    University of Texas at Austin - Austin, Texas

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