Staff ML Engineer developing multimodal foundation models from Tonal’s sensor, video, and physiological workout data. Advancing adaptive coaching, form feedback, and musculoskeletal health intelligence.
About the role
- Senior ML engineer building personalized giving recommendations and fraud-risk systems. Deploying reliable real-time AI for Givelify’s nonprofit donation platform.
Responsibilities
- 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
- 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
- Competitive pay
- Full benefits
- Flexible PTO
- Amazing perks
- Opportunity to put passion and purpose front and center
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