Senior Software Engineer - Machine Learning and AI

About Givelify

Givelify is a fintech-for-good company powering an online and mobile giving platform. Thanks to over 2 million generous donors, Givelify has helped more than 80,000 nonprofits and places of worship raise over $7.5 billion.

About the Role

As a Senior Machine Learning Engineer at Givelify, you will design, build, and ship the models that power real-time decisions across our giving platform. This includes building systems that allow donors to experience personalized causes 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 the giving community.

You will join the Research Engineering & Machine Learning team—a group of engineers and scientists tackling complex computational, machine learning, and AI problems across Givelify's giving and fintech platforms.

What You'll Do

• Frame ambiguous product problems into well-posed ML formulations (ranking, classification, anomaly detection) with clear success metrics.
• 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 collaborate with Platform/DevOps on scalability, latency, and monitoring.
• Productize machine learning systems that generate personalized recommendations for donors.
• Build AI systems consuming real-time data for diverse use cases ranging from Engineering Reliability to Fraud Prevention.
• Ensure models operate within ethical value systems and maintain production reliability and low latency.
• Mentor engineers on ML best practices and maintain high scientific rigor and code quality.

Qualifications

• Education: Master’s or PhD in Computer Science, Computer Engineering, Electrical Engineering, Statistics, Applied Mathematics, a STEM field, or equivalent research experience.
• Experience: 2+ years of building and deploying machine learning systems in production (ideally serving hundreds of thousands to millions of users).
• Mathematical Foundations: Deep grounding in probability, statistics, linear algebra, and optimization.
• ML & DL Expertise: Hands-on experience across classical ML and deep learning (e.g., gradient-boosted trees, embedding models, transformers).
• Data & Tooling: Experience with large-scale data processing (Spark, distributed training, feature stores) and modern ML tooling (PyTorch or TensorFlow, scikit-learn, MLflow or similar).
• Domain Track Record: Experience in recommender systems, fraud/risk modeling, search ranking, or real-time classification.
• Research Plus: A background in publications, thesis work, or applied research translating novel methods into shipped solutions is a strong plus.

Key Mindset & Competencies

• Integrity & Customer Focus: Human-centric approach to problem-solving with sound ethical baselines.
• Scientific Rigor: Insistence on sound baselines, honest evaluation, and reproducibility.
• Ownership Mindset: Capability to take models from whiteboard to production and stay accountable for their performance.