Senior Software Engineer – Machine Learning, AI

This is a fully remote position, open to applicants in United States, +1 more country.

📋 Description

• Design, develop, and implement machine learning models that drive real-time decisions across Givelify’s donation platform.

• Create personalized recommendation systems that connect donors with pertinent organizations and causes.

• Build AI-driven fraud detection and risk assessment systems.

• Manage machine learning challenges from start to finish, including mathematical problem framing, model training, evaluation, and deployment.

• Ensure that models align with Givelify’s core values and prevent negative experiences for donors.

• Take responsibility for the reliability of production systems and the quality of models.

• Strive to achieve product objectives while minimizing false positives and adverse experiences.

• Conduct research and prototype computational, statistical, and AI models using real-world donor data.

• Transform machine learning systems into products that offer personalized recommendations.

• Develop AI solutions utilizing large-scale real-time data to ensure engineering reliability and fraud prevention.

• Convert ambiguous product challenges into machine learning formulations that involve ranking, classification, and anomaly detection.

• Construct and maintain comprehensive ML pipelines that include feature engineering, training, evaluation, A/B testing, and deployment.

• Deliver real-time inference services and collaborate with Platform/DevOps teams on scalability, latency, and monitoring.

• Provide mentorship to engineers on best practices in machine learning, scientific rigor, and code quality.

⛳️ Requirements

• Master’s degree or PhD in Computer Science, Computer Engineering, Electrical Engineering, Statistics, Applied Mathematics, or a related STEM field, or equivalent research experience.

• A minimum of 2 years of experience in building and deploying machine learning systems in a production environment.

• Strong foundation in probability and statistics, linear algebra, and optimization techniques.

• Practical experience with classical machine learning and deep learning methods, including gradient-boosted trees, embedding models, and transformers.

• Familiarity with large-scale data processing, Spark, distributed training, feature stores, and contemporary ML tools.

• Proficiency in PyTorch or TensorFlow, scikit-learn, and MLflow or similar frameworks.

• Proven experience in recommender systems, fraud/risk modeling, search ranking, or real-time classification.

• A background in research, including publications, thesis work, or applied research is a significant advantage.

• Traits such as scientific rigor, a sense of ownership, effective communication, customer focus, integrity, and strong problem-solving skills.

🏝️ Benefits

• Competitive salary.

• Comprehensive benefits package.

• Flexible paid time off.

• Outstanding perks.

• Opportunity to align your passion with meaningful work.