Senior Software Engineer - Machine Learning and AI

Givelify is a fintech-for-good company that provides an online and mobile giving platform connecting donors with nonprofits and places of worship. The Senior Software Engineer - Machine Learning and AI will design, build, deploy, and maintain machine learning and AI systems for personalization, fraud detection, risk scoring, and real-time decision-making. The role also involves owning end-to-end ML pipelines, ensuring production reliability, and mentoring engineers on ML best practices.

Responsibilities:

• 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
• 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

Requirements:

• 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
• 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