Senior Software Engineer - Machine Learning and AI

Senior Software Engineer
- Machine Learning and AI

Givelify is a fintech-for-good company where brilliant minds come to power the most loved and trusted online and mobile giving app platform. Thanks to over 2 million generous donors, we've helped more than 80,000 nonprofits and places of worship raise over $7.5 billion. Together, they're changing their world with kindness and generosity.

Inc. 5000, the Stevies® Awards, Gartner, Forbes, and many more have recognized our story, innovations, and achievements. Our award-winning team builds products and experiences that put more good into the world. We love to take on big challenges because we know our work matters.

Make an impact at one of the fastest-growing private companies in the U.S. Be part of a talented team of big-hearted individuals, earning competitive pay with excellent benefits.

Aboutyourrole:

As a Senior Machine Learning Engineer at Givelify, you'll design, build, and ship the models that power real-time decisions across our giving platform. This includes building systems that allow our donors to experience personalized causes that they can champion and help them become more consistent in their giving journey. You will also work on AI-based fraud detection and risk-scoring systems to protect our giving community.

You'll own problems end-to-end, framing them mathematically, working with large-scale donor and transaction data, training and evaluating models, and deploying them as reliable, low-latency production services. Your work directly shapes how millions of donors find and support the causes they love — and protects every dollar they give.

Yourteam:

You'll join our Research Engineering & Machine Learning team, a group of world-class engineers and scientists. This team tacklescomplexcomputational, machine learning, and AI problems across

Givelify'sgiving and fintech platforms and products.

The Senior Software Engineer
- Machine Learning and AI will get to:

• Ensure our modelsoperatewithin our value system sand never recommend that leads our donors to havea bad experience.
• Ensure donors easily connect with the organizations and causes that matter most to them.
• Help donors become the best version of themselves by helping them realize their desire to become moreconsistentin their giving.
• Own the production reliability and quality of ourmodels.
• Maximize product goals while minimizing false positives and negative experiences for ourgivingcommunity.

To be successful in this role, you’ll also:

• Research and prototype computational, statistical, and AI models on real-world donor datasets for various real-world problems.
• Productize machine learning systems that can generate personalized recommendations for our Donors and help champion their giving journey.
• Build AI systems that can consume vast amounts of real-time data to a diverse class of problems from Engineering Reliability to Fraud Prevention.
• Work on a wide array of problems that Machine Learning and AI can be used to solve.
• Translate ambiguous product problems into well-posed ML formulations. Including ranking, classification, and anomaly detection, with clear success metrics and evaluation design.
• Build andmaintainend-to-end ML pipelines: feature engineering on large datasets, training, evaluation, A/B testing, and deployment.
• Ship real-time inference services and work with Platform/Dev Ops on scalability, latency, and monitoring.
• Mentor engineers on ML best practices and raise the team’s bar for scientific rigor and code quality.

Your experience:

• Master's or PhD in Computer Science,Computer Engineering,Electrical Engineering, Statistics, Applied Mathematics, orin aSTEMfield, or equivalent research experience.
• 2+ years of building and deploying machine learning systems in production, ideally including at least one system serving hundreds of thousands to millions of users.
• Deep grounding in the mathematics behind ML: probability and statistics, linear algebra, optimization; you understand why models work, not just how to call them.
• Hands-on experience across classical ML and deep learning (e.g., gradient-boosted trees, embedding models, transformers) and knowing when each is the right tool.
• Experience with large-scale data processing (Spark, distributed training, feature stores) and modern ML tooling (PyTorchor Tensor Flow, scikit-learn,MLflowor similar).
• Track recordin one or more of: recommender systems, fraud/risk modeling, search ranking, or real-time classification.
• Research background publications, thesis work, or applied research,…