Senior Software Engineer – Machine Learning, AI

Givelify

. Design, build, and ship machine learning models powering real-time decisions across Givelify’s giving platform .

Posted 10/22/2026full-timeRemote • United States, CanadaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in designing and deploying machine learning models for real-time decision-making, with a strong foundation in probability, statistics, and optimization. Proven ability to build personalized recommendation systems and AI-based fraud detection solutions while ensuring model quality and reliability.

Highest-signal resume keywords

Machine Learning Model DevelopmentRecommendation SystemsFraud Detection SystemsPyTorch or TensorFlow ExperienceLarge-Scale Data Processing

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningProbability and StatisticsLinear AlgebraOptimizationGradient-Boosted TreesEmbedding ModelsTransformersFeature EngineeringA/B Testing

Soft Skills

Effective CommunicationOwnership MindsetCustomer FocusIntegrityProblem-Solving

Tools & Technologies

SparkDistributed TrainingFeature StoresMLflowScikit-learn

Certifications & Qualifications

Master’s or PhD in Computer ScienceComputer EngineeringElectrical EngineeringStatisticsApplied Mathematics

Industry Keywords

Real-Time InferenceEnd-to-End ML PipelinesModel EvaluationModel DeploymentResearch Background

Tech Stack

Tools & technologies

PyTorchScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact

Design, build, and ship machine learning models powering real-time decisions across Givelify’s giving platform
Build personalized recommendation systems connecting donors with relevant organizations and causes
Develop AI-based fraud detection and risk-scoring systems
Own ML problems end-to-end, including mathematical framing, model training, evaluation, and deployment
Ensure models operate within Givelify’s value systems and avoid negative donor experiences
Own production reliability and model quality
Maximize product goals while minimizing false positives and negative experiences
Research and prototype computational, statistical, and AI models using real-world donor datasets
Productize machine learning systems for personalized recommendations
Build AI systems using large-scale real-time data for engineering reliability and fraud prevention
Translate ambiguous product problems into ML formulations involving ranking, classification, and anomaly detection
Build and maintain end-to-end ML pipelines covering feature engineering, training, evaluation, A/B testing, and deployment
Ship real-time inference services and collaborate with Platform/DevOps on scalability, latency, and monitoring
Mentor engineers on ML best practices, scientific rigor, and code quality

Requirements

What you’ll need

Master’s or PhD in Computer Science, Computer Engineering, Electrical Engineering, Statistics, Applied Mathematics, or a STEM field, or equivalent research experience
2+ years of building and deploying machine learning systems in production
Deep grounding in probability and statistics, linear algebra, and optimization
Hands-on experience with classical ML and deep learning, including gradient-boosted trees, embedding models, and transformers
Experience with large-scale data processing, Spark, distributed training, feature stores, and modern ML tooling
Experience with PyTorch or TensorFlow, scikit-learn, and MLflow or similar
Track record in recommender systems, fraud/risk modeling, search ranking, or real-time classification
Research background, publications, thesis work, or applied research is a strong plus
Scientific rigor, ownership mindset, effective communication, customer focus, integrity, and problem-solving

Benefits

Comp & perks

Competitive pay
Full benefits
Flexible PTO
Amazing perks
Opportunity to put passion and purpose front and center