Senior Software Engineer - Machine Learning and AI

Accountabilities:: Research, prototype, and evaluate computational, statistical, machine learning, and AI models using real-world donor and transaction datasets. Translate ambiguous product challenges into well-defined ML problems, including ranking, classification, anomaly detection, and recommendation use cases, with clear success metrics and evaluation methodologies. Design and productize machine learning systems that deliver personalized recommendations and help users connect with the organizations and causes most relevant to them. Build AI systems capable of processing large volumes of real-time data across applications such as fraud prevention, risk scoring, engineering reliability, and other business and product challenges. Develop and maintain end-to-end ML pipelines covering feature engineering, large-scale data processing, model training, evaluation, experimentation, A/B testing, and deployment. Build and operate real-time inference services in partnership with Platform and DevOps teams, ensuring scalability, low latency, reliability, and effective monitoring. Own the production quality and reliability of models, continuously balancing product objectives with false positives and negative user experiences. Ensure model behavior aligns with responsible product principles and supports positive, trustworthy experiences. Mentor engineers on machine learning best practices while raising standards for scientific rigor, reproducibility, and code quality. Requirements: 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. Benefits: Competitive compensation. Comprehensive benefits designed to support employees’ health and long-term well-being. Flexible paid time off. Additional employee perks and support programs. Fully virtual work environment with opportunities to collaborate with high-performing professionals. Meaningful work at the intersection of machine learning, AI, personalization, fraud prevention, and real-time decision-making. Opportunity to contribute to products designed around human needs and positive social impact. Collaborative and inclusive culture that values diversity, integrity, innovation, and purpose. Opportunities for professional growth, mentorship, and continuous learning.