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Zach Boldyga, Software Developer in Seattle, WA, United States
Zach Boldyga

Software Developer in Seattle, WA, United States

Member since February 28, 2017
Zach’s abilities are best captured through his recent, six-year founding and management of Scalabull, a software company that currently powers over 1 million patient lab testing transactions in the US annually. Today, he's actively seeking to help organizations have a positive impact thru aiding with strategy in machine learning and software engineering.
Zach is now available for hire


  • Scalabull
    Python, Jupyter Notebook, TensorFlow, Scikit-learn, Node.js, Rust, Mongo...
  • TerraClear
    AWS Lambda, SQS, RDS, EC2, S3, Node.js, PostgreSQL, TypeScript
  • RxNT
    JavaScript, Ruby, VB.NET, C#.NET


  • JavaScript, 11 years
  • Python 3, 4 years
  • MongoDB, 4 years
  • Scikit-learn, 2 years
  • TensorFlow, 2 years
  • Rust, 1 year
Seattle, WA, United States



Preferred Environment

*nix, JavaScript or Python, TensorFlow, MongoDB

The most amazing...

...thing I've done is found Scalabull, a backbone of US healthcare infrastructure!


  • Founder | CTO

    2013 - PRESENT
    • Architected and managed a technology stack with over 150 microservices; implemented over 100 of the microservices.
    • Built a platform for submitting and receiving medical data to/from hospitals, physician's offices, and laboratories in a variety of formats—resulting in a guaranteed real-time message delivery.
    • Leveraged millions of healthcare datapoints to implement machine learning tools that optimize workflows and provide non-trivial insights.
    • Ensured that HIPAA security best-practices are used throughout the organization.
    • Managed four different product teams over the company lifespan. In chronological order: Built a data warehouse, completed Quest Diagnostics & LabCorp certified integrations, built a cloud-based platform for interface management, developed machine learning APIs.
    Technologies: Python, Jupyter Notebook, TensorFlow, Scikit-learn, Node.js, Rust, Mongo, Elasticsearch, Redis,, ZeroMQ, Meteor, React, Ruby, CouchDB, MySQL
  • Software Engineering Contractor

    2018 - 2019
    • Served as a stopgap in an early stage startup while the founders searched for dedicated employees. Discussions with the team spanned machine learning, software engineering, and devOps strategies.
    • Developed a pipeline to incorporate machine learning into the flagship application.
    • This was an opportunity to work with an all-star cast of tech industry veterans. I learned a lot, and was able to provide value in a time of need.
    • Leveraged the AWS ecosystem, with loose coupling for optionally switching to locally-hosted solutions or other vendors.
    • Built command line tools for submitting and monitoring ML jobs.
    • Built an open source service mesh that we forked internally; all done during the course of the above work.
    Technologies: AWS Lambda, SQS, RDS, EC2, S3, Node.js, PostgreSQL, TypeScript
  • Seasonal Software Engineering Intern

    2008 - 2012
    • Developed a TWAIN driver for electronic signature processing.
    • Created tools for processing messy text data.
    • Implemented rudimentary web applications for marketing and internal use.
    • Fixed bugs in the production application.
    • Helped implement new features in the production application.
    Technologies: JavaScript, Ruby, VB.NET, C#.NET


  • Implementation of Conv3d for TensorFlow.js (Development)

    I implemented 3D convolutions for TensorFlow.js:

    TensorFlow is written in C++ and CUDA and exposes a C API which is then called by other, easier-to-use languages like Python. For nearly all programming languages, using TensorFlow simply means building a client library by writing some wrapper code around the C API.

    However, enabling a tool like TensorFlow to run via JavaScript in a web browser is a bit different. With WASM, it's technically possible to run this C++/CUDA code in the browser. But as of 2019, the practical approach to doing inferencing and training of machine learning models inside of a web browser is to use a tool like TensorFlow JS: a from-scratch implementation of TensorFlow's C++ and CUDA code, instead of being written in TypeScript and WebGL.

    That said building TensorFlow JS means rebuilding all of the components of a machine learning library from scratch. Aiding in this effort, I built in the ability for this library to handle 3D convolutions that are commonly used in video and time series applications.

    This technology currently powers web-based 3D medical image segmentation tools, video effects, and more!

  • Predicted Delivery Times for Lab Results (Development)

    US lab results contain a number of timestamps (always including the original time the test was ordered, and the time the testing was completed and when the result was sent to the patient). Using millions of these examples, a neural-network-based regression model was trained to predict when the ordered test results will be delivered to patients.

    If a physician orders a TSH panel from Quest Diagnostics—due to the fact that we've seen over 50,000 of those transactions—our system has quality information to assess when the result will be returned. While relying on deep learning allowed us to avoid complex feature extraction in this implementation, the initial implementation used multivariate linear regression and required feature extraction. Therefore, it's clear that the neural network relies on information like the day of the week and time of day the test was ordered, the type of test, the age of the patient, and the lab and/or region of the US.

    This is currently considered proprietary; the code is not included.

  • AltFlo Service Mesh (Development)

    I developed a microservice mesh to ease adoption of microservices in SMEs.

    As of 2019, there's an emerging space around service meshes as a means to simplify management of an organization or team's software products.

    On one end of the spectrum, tools like Istio and Consul provide container-based services over Kubernetes, along with a boatload of features for A/B testing, monitoring, caching, and more. These tools are well-suited for mass-scale deployments and large teams.

    But for small teams and projects that will never grow beyond the likes of a dozen machines, building small programs that communicate via a messaging queue (Amazon SQS, Nsq.IO, Redis, RabbitMQ) is pragmatic.

    AltFlo is an extension of the lightweight, queue-based approach. It prevents boilerplate, allows the creation of service graphs, captures metrics for monitoring, provides transactional guarantees, and has a pluggable back-end for your queue of choice.

    It also allows separation of application code from business logic. Workflows that connect multiple microservices together with conditional behavior can be created with only 10 lines of code!

  • 100+ Scalabull Applications (Development)

    I architected, built, and performed DevOps for over 100 of the microservices that power Scalabull.

    • An entire, medical-grade data warehouse.
    • User-facing platforms built with React, Meteor, chart.js
    • REST APIs
    • Generation of a variety of clinically-approved PDF formats with conditionally changing layouts and barcodes.
    • Integration into SOAP web services provided by Quest Diagnostics and LabCorp.
    • Created more than 1,000 HL7 interfaces.Live connections between doctors and clinical laboratories.
    • MLLP server and client.
    • In-house SFTP server and client.
    • Machine learning tools with production-grade deployments.
    • ZeroMQ, Redis, and NSQ used for various messaging and key/value store needs.
    • Elasticsearch for information discovery.
    • Nagios to track system failures and provide real-time alerts to developers.
    • Fault tolerant infrastructure with combined hardware firewalls, dedicated servers, and cloud servers.

  • MxNet *.random Documentation (Development)

    I documented the expected behaviors of a Python library for functionalities in nd.random and sym.random. I developed a good understanding of statistical systems and MxNet and used these to help future developers adopt these tools more quickly and use them more accurately.

  • React JSON-schema Form Ecosystem (Development)

    I orchestrated and contributed to the development of multiple open source extensions to the react-jsonschema-form, a utility for creating user input forms from a JSON schema object on-the-fly. Our plugins allow users to significantly enhance this tool and create enterprise-grade forms from JSON-based configurations.

    The projects include:
    • Extras: an assortment of fancy widgets (e.g., typehead text fields).
    • Pagination: multipage forms are supported.
    • Conditionals: a rules engine can be applied to compute values in the form, hide form fields, and manipulate form CSS.
    • Form Manager: This makes it simpler to plug all of our utilities together into an enterprise-grade application.


  • Languages

    JavaScript, Python 3, TypeScript, Rust, C, Visual Basic .NET (VB.NET), C#.NET
  • Frameworks

  • Libraries/APIs

    TensorFlow, Scikit-learn, NumPy, React
  • Paradigms

    Functional Programming, Object-oriented Programming (OOP), Gang of Four (GOF) Design Patterns
  • Platforms

    Jupyter Notebook, Linux, *nix, Rackspace
  • Tools

    Amazon SQS
  • Storage

    MongoDB, CouchDB, PostgreSQL, Redis, MySQL, AWS RDS


  • Completed the program in Alternative MBA Studies
    2018 - 2018
    altMBA - Online (
  • Bachelor of Science degree in Computer Science
    2008 - 2011
    University of Maryland, College Park - College Park, MD, USA
  • Deep Learning Specialization
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