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Senior Machine Learning EngineerGiveCampus • Richmond, Virginia, United States
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Senior Machine Learning Engineer

GiveCampus
  • US
    Richmond, Virginia, United States
  • US
    Richmond, Virginia, United States
Apply Now

About

Overview
GiveCampus is the world's leading fundraising platform for non-profit educational institutions. Trusted by 1,300+ colleges, universities, and K-12 schools, our mission is to help advance the quality, the affordability, and the accessibility of education. We achieved six consecutive years of profitability and positive cash-flow while expanding our revenue, customer base, and team. In 2022, we raised $50 million to accelerate the next stage of growth. We’ve facilitated more than $6 billion in charitable giving and operate at a meaningful scale while remaining mission-driven and focused on impact. Our purpose-driven team of 120+ is located across the US: team members work from anywhere they choose. We have a 12,000 sq ft office in Washington, DC available for people to use, and we regularly organize team meet-ups, events, and retreats in various locations. We are expanding our team with diverse and collaborative doers who believe in our mission and the transformative power of affordable, high-quality education. Location:
This is a remote-first role based in the U.S. While we embrace flexible, distributed work, we also value in-person connection. Team members are expected to attend multiple company-wide and team-specific onsites throughout the year. We are looking for a Senior ML Engineer to own the productionization and operational lifecycle of our machine learning models. You will work closely with our Data Scientist to take validated models from notebooks to production systems that serve predictions to our customers. This is our first ML Engineer position, and you will be instrumental in defining the direction of our ML Platform. This is a high-impact role where you will shape how we build and operate ML systems. You will be responsible for the full journey from prototype handoff through deployment, monitoring, and ongoing maintenance. Over time, you will build reusable tooling and self-service capabilities that enable faster iteration between Data Science and Production—reducing handoff friction and accelerating time-to-value for new models.
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  • Richmond, Virginia, United States

Languages

  • English
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