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.