Machine Learning Engineer

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🕒 March 31

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Givzey

WebsiteLinkedInAll Job Openings

1 - 10 employees

💸 Finance

🤝 Non-profit

☁️ SaaS

Finance • Non-profit • SaaS

Givzey is a platform that offers intelligent solutions for the fundraising process, including smart gift agreements, dynamic workflows, automated pledge reminders, and intelligent invoicing. The company aims to streamline and scale multi-year giving and one-time gifts, enhancing the donor experience while providing tools for fundraising teams to improve efficiency. Givzey is utilized by educational institutions and other organizations to secure and manage donations effectively.

📋 Description

• Design, develop, and implement machine learning models and algorithms • Analyze large datasets and extract meaningful insights • Collaborate with cross-functional teams to integrate ML solutions into existing systems • Optimize ML models for performance and scalability • Stay current with the latest advancements in machine learning and AI • Create and implement big data processing pipelines and architectures • Design and build scalable data infrastructure to support ML applications

🎯 Requirements

• US Citizen • Bachelor's or Master's degree in Computer Science, Data Science, or related field • 3+ years of experience in machine learning or AI development • Strong proficiency in Python and its ML/data science libraries (e.g., TensorFlow, PyTorch, scikit-learn, pandas) • Solid understanding of machine learning algorithms and statistical modeling • Experience with big data technologies (e.g., Hadoop, Spark, Kafka) • Proven ability to create and implement big data solutions from scratch • Comfortable setting up and managing distributed computing environments • Experience in designing and implementing data pipelines for large-scale data processing • Proficient in working with various database systems, both SQL and NoSQL, depending on the use case • Strong skills in data modeling and database design for machine learning applications

🏖️ Benefits

• Health insurance • Flexible work arrangements • Professional development