Colleen Kinross

Senior Software Engineer at MongoDB

Toronto, Ontario, Canada

About

Experience

  • Senior Software Engineer at MongoDB
    Apr 2026 - Present · 3 mos

    MongoDB Search for Community and Enterprise Edition

  • Google (5 yrs)
    • Senior Software Engineer
      Nov 2025 - Apr 2026 · 6 mos

    • Senior Software Engineer
      May 2024 - Nov 2025 · 1 yr 7 mos

    • Software Engineer
      May 2021 - May 2024 · 3 yrs 1 mo

  • Microsoft (3 yrs 11 mos)
    • Software Engineer II
      Nov 2018 - May 2021 · 2 yrs 7 mos

      Working on "ingestion" (publishing) and configuration in the gaming developer experiences org. In this role I've gone from backend to full stack working with C# on the backend and Typescript on the frontend with React + Redux. I've gotten to work on many projects including, most recently, delivering a new Xbox disc authoring experience for publishers releasing for the upcoming console.

    • Software Engineer
      Jul 2017 - Nov 2018 · 1 yr 5 mos

      Software Engineer (Backend) on the Yammer team! My work includes meeting compliance requirements, helping to maintain and improve our micro service architecture and feature development. I work in the messaging domain which includes anything in the posting to reading path of messages. I've also gotten to work on deletion projects and spent some time in our search domain which is backed by elasticsearch.

  • Graduate Student, Computer Science at University of Waterloo
    May 2015 - Nov 2017 · 2 yrs 7 mos

    I was part of the Scientific Computation Group: http://scicom.uwaterloo.ca/. During my program I was co-supervised by Prof. Yuying Li and Prof. Justin Wan. My research was in the area of machine learning and optimization. Specifically, I studied the effectiveness of trust region methods for training feedforward neural networks. My thesis is available at https://uwspace.uwaterloo.ca/handle/10012/12621.

  • Software Engineer - Machine Learning Intern at Splunk
    Sep 2016 - Dec 2016 · 4 mos

    Worked with the machine learning team. We designed and prototyped a new anomaly detector using relationships between two timeseries. Relationships determined through models such as neural networks, and other models.