Senior Software Engineer - Machine Learning and AI

Givelify

22h•Remote

About The Position

As a Senior Machine Learning Engineer at Givelify, you will design, build, and ship models that power real-time decisions across the giving platform. This includes personalized cause recommendations that help donors build consistent giving habits, as well as AI-based fraud detection and risk-scoring systems. You will own problems end to end: formulate them mathematically, work with large-scale donor and transaction data, train and evaluate models, and deploy reliable, low-latency production services. Your work will shape how donors find and support causes and help protect their donations. You will join the Research Engineering & Machine Learning team, which works on computational, machine learning, and AI problems across Givelify’s giving and fintech platforms and products. The role focuses on building useful, reliable models that connect donors with organizations and causes, improve their giving journeys, and minimize false positives and negative experiences. You will research and prototype models, translate product problems into measurable ML formulations, develop end-to-end pipelines, ship real-time inference services, and mentor engineers on ML best practices.

Requirements

Master’s or PhD in Computer Science, Computer Engineering, Electrical Engineering, Statistics, Applied Mathematics, or another STEM field, or equivalent research experience.
At least 2 years of experience building and deploying machine learning systems in production.
Deep understanding of probability and statistics, linear algebra, and optimization as applied to machine learning.
Hands-on experience with classical machine learning and deep learning, such as gradient-boosted trees, embedding models, and transformers, and judgment about when to use each.
Experience with large-scale data processing, such as Spark, distributed training, and feature stores, and modern ML tools such as PyTorch or TensorFlow, scikit-learn, and MLflow or similar.
A track record in at least one of recommender systems, fraud/risk modeling, search ranking, or real-time classification.
Scientific rigor, including sound baselines, honest evaluation, and reproducibility.
Ownership mindset and accountability for models from development through production.

Nice To Haves

Experience deploying a production system serving hundreds of thousands to millions of users.
Research experience such as publications or thesis work, or applied research translating novel methods into shipped solutions.

Responsibilities

Ensure models operate in accordance with Givelify’s values and do not lead donors to a bad experience.
Help donors connect with organizations and causes that matter to them and become more consistent in their giving.
Own the production reliability and quality of models.
Maximize product goals while minimizing false positives and negative experiences for the giving community.
Research and prototype computational, statistical, and AI models using real-world donor datasets.
Productize machine learning systems that generate personalized recommendations for donors.
Build AI systems that process large volumes of real-time data for problems including engineering reliability and fraud prevention.
Translate ambiguous product problems into well-defined ML formulations, including ranking, classification, and anomaly detection, with clear success metrics and evaluation designs.
Build and maintain end-to-end ML pipelines, including feature engineering, training, evaluation, A/B testing, and deployment.
Ship real-time inference services and collaborate with Platform/DevOps on scalability, latency, and monitoring.
Mentor engineers on ML best practices and improve scientific rigor and code quality.