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About

Originally from Christchurch, New Zealand, after obtaining a US Green Card I moved to Boston, Massachusetts. I am a Machine Learning Engineer with 8 years of experience building production systems at TB/PB scale. I have a Physics background and a track record of measurable business impact including foundational tech for a startup. Boston is full of bright people and I am always down to meet them.

Career

From February 2018 to October 2025 my main employer was Ströer Labs NZ, a European AdTech company.
I started working for Ströer as a Java-based Software Engineer before being given the opportunity to help start a data team for our New Zealand branch. I taught myself entire fields and technologies as needed, delivering projects that increased revenue by tens of millions of Euros annually while cutting cloud costs by tens of thousands per month. This gave me versatile experience across the data spectrum; from Data Engineering, through Analytics and Modelling, to Machine Learning Operations. Building production systems that handle trillions of rows of data annually taught me how to work efficiently with large datasets and solve interesting deployment problems. This also gave me plenty of opportunities to work closely and cross-functionally with employees across the global offices. Other hats I wore during my time at Ströer included; Software Engineering, mentoring junior members, taking on interns, helping with recruitment, third party business assessment and collaboration, and attending conferences.
Our tech stack was hosted on Amazon Web Services (AWS) with a large focus on opensource technologies to reduce reliance on first-party solutions.

After completing my MSc, I was sought out by Pyper Vision, an aerospace startup with the goal of eliminating fog disruptions at airports. They offered me some contracting work to do alongside my work at Ströer and I happily accepted the challenge to apply my technical skills to a new industry. The first contract involved helping them transition from a hardware company to a software one by building a fog forecasting product. I enjoyed working closely with their founding team to shape the path of their company. We chose to host their tech stack on Google Cloud Platform (GCP) with an emphasis on platform-agnostic technologies to lessen the vendor-locking risk down the line. I was able to build them a commercially competitive solution within 8 months. Pyper Vision was able to successfully launch this product and achieve a valuation in the tens of millions. After several months building their team they reached out to me again for a second contract to build a fog type classifier. This project involved taking inspiration from recent meteorological research papers and adapting them to our data and pipelines. I was able to complete this contract in 4 weeks.

Building Pyper Vision’s foundational tech proved I could apply large-scale ML systems to new domains effectively. This experience convinced me that production ML systems combined with physics intuition can solve real aerospace problems, from atmospheric modeling to the autonomous systems needed for space operations.

Academia

From February 2015 to November 2017, I completed a Bachelor’s of Science at the University of Canterbury, majoring in both Physics and Computer Science. The small amount of research I did in my undergraduate studies was in Solid State Physics, creating thin films of Yttrium Silicate via pulsed laser deposition as proposed candidates for quantum computing. While the technique was rough, we were able to show that it was possible to produce this particular form of quantum semiconductor (rare-Earth doped YSO) in situ.

From July 2022 to July 2024 (concurrently with Ströer Labs NZ), I completed a Master’s of Science at the University of Canterbury, majoring in Physics. This was with the Atmospheric Science department, under the guidance of Dave Frame and Suzanne Rosier. My particular research niche was extreme weather events over New Zealand, particularly precipitation, and how these differ in simulated worlds with and without anthropogenic climate change. I enjoyed the challenge of adapting my skills learnt from AdTech to thousands of years of simulated geospatial data. During this time I was given the opportunity to attend and speak at a national meteorology conference.