Job description

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. About your role: 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. Your team: You’ll join our Research Engineering & Machine Learning team, a group of world-class engineers and scientists. This team tackles complex computational, machine learning, and AI problems across Givelify’s giving and fintech platforms and products. The Senior Software Engineer - Machine Learning and AI will get to: Ensure our models operate within our value systems and never recommend that leads our donors to have a 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 more consistent in their giving. Own the production reliability and quality of our models. Maximize product goals while minimizing false positives and negative experiences for our giving community. 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 and maintain end-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/DevOps 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, or in a STEM field, 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 (PyTorch or TensorFlow, scikit-learn, MLflow or similar). Track record in one or more of: recommender systems, fraud/risk modeling, search ranking, or real-time classification. Research background publications, thesis work, or applied research, translating novel methods into shipped solutions, is a strong plus. Your superpowers: Integrity: Demonstrate congruence in thought, speech, and action. Can be trusted to act with courage and to do the right thing.   Problem-Solving - Resourceful and creative in solving complex issues. Use sound judgment, data, and collaboration.   Scientific rigor: you insist on sound baselines, honest evaluation, and reproducibility. Ownership mindset: you carry models from whiteboard to production and stay accountable for their behavior. Effective Communication - Communicate frequently using clarity and appropriate methods/tools of communication.    Customer Focus: Adopting a human-centric approach that meets or anticipates customer needs when developing solutions.     Our culture: We are a virtual team of award-winning and high-performing professionals who innovate and collaborate to fulfill our mission to instantly connect people to causes that matter most to them so they can change their world with kindness and generosity. Our four keys to success - integrity, heart, simplicity, and wow - fuel our passion to be recognized among the tech industry’s most inclusive and purpose-driven workplaces. We are steadfast in our conviction to overcome challenges while performing meaningful work alongside some of the most brilliant minds and biggest hearts. It propels our growth as individuals and as a team. We are committed to respecting each other in our workplace, firmly believing diversity is our strength. At the heart of everything we do are our giving community, our donors and our partner organizations. We lean on research and human-centered design to consistently push the envelope and innovate our products and customer experiences. We take great pride in providing competitive pay, full benefits to help care for you today and in the future, amazing perks (including flexible PTO), and, most importantly, the opportunity to put passion and purpose front and center. About Givelify Givelify is the most loved and trusted online and mobile giving platform. Along with its powerful donation management system, it’s the fastest-growing technology for advancing generosity in the world. We instantly connect people to their heart’s impulse to do good with award-winning products and experiences. A global community of over 2 million generous people supports their favorite churches, places of worship, nonprofits, and causes with over $7.5 billion in donations across more than 80,000 organizations. Givelify leads all giving apps on the App Store and Google Play Store with more than 135,000 verified, authentic reviews with an average 4.9 out of 5-star rating. Learn more at Givelify.com . Ready to join the Givelify team? Apply below.

Requirements

Must-haves

Senior Software Engineer - Machine Learning and AI
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.
The Senior Software Engineer - Machine Learning and AI will get to:
Ensure our models operate within our value systems and never recommend that leads our donors to have a 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 more consistent in their giving.
Own the production reliability and quality of our models.
Maximize product goals while minimizing false positives and negative experiences for our giving community.
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 and maintain end-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/DevOps on scalability, latency, and monitoring.
Mentor engineers on ML best practices and raise the team’s bar for scientific rigor and code quality.
Master’s or PhD in Computer Science, Computer Engineering, Electrical Engineering, Statistics, Applied Mathematics, or in a STEM field, 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 (PyTorch or TensorFlow, scikit-learn, MLflow or similar).
Track record in one or more of: recommender systems, fraud/risk modeling, search ranking, or real-time classification.
Research background publications, thesis work, or applied research, translating novel methods into shipped solutions, is a strong plus.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningaiml

Platform: greenhouse
Effort: quick

Benefits

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