Senior Software Engineer - Machine Learning and AI

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Software Engineer - Machine Learning and AI based in United States. 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. 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.