Haydn Vestal, Developer in Austin, TX, United States
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Haydn Vestal

Verified Expert  in Engineering

Software Engineer and Developer

Austin, TX, United States
Toptal Member Since
April 7, 2015

Haydn has a wide variety of experience across companies, from a 2-person startup to Goldman Sachs and Google. Haydn's area of greatest consulting expertise is small- to mid-stage startups, but he also has experience with automated trading and Android development.


Rust, Python 3, System Architecture, API Databases, APIs, Concurrency...
Google Cloud Spanner, Python, C++, Java, Distributed Systems...
Goldman Sachs
MongoDB, Apache Tomcat, Java, Distributed Systems




Preferred Environment

Git, Python, Java, Rust, IntelliJ IDEA, Linux, CLion

The most amazing...

...product that I've worked on is Google Jamboard, the 1st hardware product from Google Workspace.

Work Experience


2020 - PRESENT
  • Developed a cloud service runtime featuring automatic cross-service transactions, automatic concurrency, and automatic GPU acceleration.
  • Created a Python client that cross-compiled Python application code to TinyChain's native JSON representation.
  • Built a self-sustaining business by offering custom software design and development services.
Technologies: Rust, Python 3, System Architecture, API Databases, APIs, Concurrency, GPU Computing, Microservices, Machine Learning, Neural Networks, CTO, Distributed Systems, Low-level Programming, Deep Learning

Software Engineer

2015 - 2020
  • Developed a low-energy Bluetooth (BLE) communication protocol for Google Jamboard and Android and iOS Jamboard apps.
  • Designed and implemented customer identity authorization for Google Jamboard.
  • Developed the tools needed to keep Google's fleet of videoconferencing devices up and running on a day-to-day basis.
Technologies: Google Cloud Spanner, Python, C++, Java, Distributed Systems, Low-level Programming, Deep Learning


2013 - 2015
Goldman Sachs
  • Worked on Orbit Suite, Goldman's set of productivity apps: wsj.com/articles/goldman-sachs-to-spin-out-mobile-phone-software-projects-into-separate-venture-1445981960.
  • Developed the web version of Orbit Drive, Goldman's internal file-sharing app.
  • Developed the Android version of Orbit Drive, Goldman's internal file-sharing app.
Technologies: MongoDB, Apache Tomcat, Java, Distributed Systems

Software Engineer

2012 - 2013
SNAP Interactive
  • Supported one of the most popular dating apps on Facebook.
  • Maintained and improved a high-traffic (around 70 million users) service running on Linux/Apache/PHP/MySQL.
  • Spearheaded a major project to re-architect a search index/matching algorithm.
  • Created a machine learning tool able to inform a user if they try to send a message that is unlikely to generate a response.
  • Went from almost 0% to almost 100% unit test coverage.
Technologies: Linux, Apache, Memcached, MongoDB, MySQL, PHP

Software Engineer

2011 - 2012
  • Maintained and expanded Hotlist, a geo-social event aggregation startup.
  • Wrote a data ingester in Python to match data from Facebook Events, Google Maps, Foursquare, Citygrid, and more.
  • Participated in the re-architecture of the database deployment.
  • Wrote a machine learning tool to match address data in different formats.
  • Wrote a machine learning tool to identify duplicate venue and event records.
Technologies: Linux, Solr, MongoDB, MySQL, Python


A single-file sample for a company that operates an ad exchange—given an HTTP endpoint with a parameter advertiser_id and an unpredictable response time which returns data in the form:

advertiser_id: "1234",
ymd: "2014-09-24",
num_clicks: 5,
num_impressions: 1090
advertiser_id: "1234",
ymd: "2014-09-25",
num_clicks: 19,
num_impressions: 1089

aggregate as much data as possible in 200ms and notify the caller of the unavailable advertiser_ids. It can be tested by calling the aggregate method with various lists of longs and examining the result. Still, it does require an HTTP endpoint fitting the description mentioned above.


An automated stock-trading program developed to use machine learning and sentiment analysis to generate trading signals.

The program works by scraping public news sources to identify news related to publicly-traded companies, then analyzing changes in the sentiment of the news related to a given company in order to predict how its stock price will change in the near future.


Rust, Python, Java, PHP, C++, HTML, JavaScript, Erlang, Python 3


Rapid Prototyping, Agile Software Development, Microservices, Functional Programming, DevOps, Data Science


MySQL, Xapian, Google Cloud Spanner, Memcached, MongoDB, API Databases


Prototyping, Software Architecture, System Architecture, CTO, Distributed Systems, Low-level Programming, Lean Startups, OpenSCAD, APIs, Concurrency, GPU Computing, Machine Learning, Neural Networks, Data Structures, Algorithms, Chemistry, IT Support, Performance, Deep Learning


Linux, Oracle, Eclipse, Amazon EC2, Android, Amazon Web Services (AWS)


Google API, Facebook API


Apache Tomcat, Apache, NGINX, Git, EAGLE, Ansible, Solr, IntelliJ IDEA, CLion

2007 - 2008

Coursework Toward a Degree in Computer Science

University of Texas - Austin, Texas, USA

2005 - 2006

Coursework Toward a Degree in Chemistry

New York University - New York, NY, USA

Collaboration That Works

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