Senior Software Engineer - Machine Learning and AI

As a Senior Software Engineer specializing in Machine Learning and AI, you’ll build and deploy intelligent systems that power real-time decisions across a large-scale digital giving platform.
You’ll work on personalization, recommendations, fraud detection, risk scoring, and other high-impact applications of machine learning and AI.
The role combines deep technical problem-solving with end-to-end ownership, from mathematical formulation and experimentation through production deployment.
You’ll work with large-scale donor and transaction datasets while building reliable, low-latency services that operate at significant scale.
Your models will directly influence how people discover meaningful causes, develop consistent giving habits, and experience safe and trusted transactions.
As part of a research-focused engineering team, you’ll collaborate with experienced engineers and scientists while contributing to a purpose-driven, human-centered environment.
The role also offers opportunities to mentor others, strengthen engineering and scientific standards, and solve complex problems where machine learning can create meaningful impact.

• Master’s or PhD in Computer Science, Computer Engineering, Electrical Engineering, Statistics, Applied Mathematics, another STEM discipline, or equivalent research experience.
• 2+ years of experience building and deploying machine learning systems in production, ideally including systems serving hundreds of thousands to millions of users.
• Strong mathematical foundation in probability and statistics, linear algebra, and optimization, with an ability to understand the underlying principles behind model behavior.
• Hands-on experience with both classical machine learning and deep learning approaches, such as gradient-boosted trees, embedding models, and transformers, with sound judgment about when to apply each technique.
• Experience with large-scale data processing, distributed training, feature stores, and modern ML tooling such as Spark, PyTorch or TensorFlow, scikit-learn, and MLflow or similar platforms.
• Demonstrated experience in one or more relevant areas, such as recommender systems, fraud or risk modeling, search ranking, or real-time classification.
• Research experience through publications, thesis work, or applied research, with the ability to translate novel methods into production solutions, is a strong plus.
• Strong problem-solving skills, with the ability to approach complex challenges creatively using data, sound judgment, and collaboration.
• Scientific rigor, including a commitment to strong baselines, honest evaluation, reproducibility, and measurable outcomes.
• Strong ownership mindset and accountability for taking ML systems from experimentation and design through production and ongoing performance.
• Clear and effective communication skills, with the ability to collaborate across technical and product teams.
• A customer-focused and human-centered approach to building solutions that anticipate user needs and promote positive experiences.
• High integrity and sound judgment, with a commitment to acting responsibly and making principled decisions.