Senior Software Engineer - Machine Learning and AI

Givelify

16h•Remote

About The Position

Givelify is hiring a Senior Software Engineer for Machine Learning and AI to design, build, and ship models that power real-time decisions across its giving platform. The role focuses on personalizing causes for donors, supporting consistent giving, and developing AI-based fraud detection and risk-scoring systems. The engineer will own problems end to end: framing them mathematically, working with large-scale donor and transaction data, training and evaluating models, and deploying reliable, low-latency production services. The role is part of Givelify’s Research Engineering & Machine Learning team, which works on computational, machine learning, and AI challenges across the company’s giving and fintech products. The engineer will help ensure models provide a positive donor experience, connect donors with relevant organizations and causes, and protect the giving community while maintaining production reliability and model quality. Givelify describes itself as a virtual team and emphasizes integrity, heart, simplicity, inclusion, research, and human-centered design. It offers competitive pay, full benefits, and perks including flexible PTO.

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 5 years of experience building and deploying machine learning systems in production.
Experience with at least one production machine learning system serving hundreds of thousands to millions of users.
Strong grounding in probability and statistics, linear algebra, and optimization.
Hands-on experience with classical machine learning and deep learning, such as gradient-boosted trees, embedding models, and transformers.
Experience with large-scale data processing, such as Spark, distributed training, or feature stores.
Experience with modern machine learning tools such as PyTorch or TensorFlow, scikit-learn, and MLflow or similar.
A track record in one or more of recommender systems, fraud or risk modeling, search ranking, or real-time classification.
Scientific rigor, including sound baselines, honest evaluation, and reproducibility.
An ownership mindset and accountability for models from development through production.
Effective communication, problem-solving, integrity, and a customer-focused approach.

Nice To Haves

Research experience such as publications, thesis work, or applied research, with the ability to translate novel methods into shipped solutions.

Responsibilities

Research and prototype computational, statistical, and AI models using real-world donor datasets.
Develop personalized recommendation systems that help donors discover causes and support their giving journey.
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 machine learning tasks, including ranking, classification, and anomaly detection, with clear success metrics and evaluation plans.
Build and maintain end-to-end machine learning pipelines, including feature engineering, training, evaluation, A/B testing, and deployment.
Ship real-time inference services and collaborate with Platform and DevOps teams on scalability, latency, and monitoring.
Own the production reliability and quality of models.
Maximize product goals while minimizing false positives and negative experiences for donors.
Ensure recommendations align with company values and provide donors with a positive experience.
Mentor engineers on machine learning best practices and promote scientific rigor and code quality.