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GiveCampus is the world's leading fundraising platform for non-profit educational institutions. Trusted by millions of donors and 1,300+ colleges, universities, and K-12 schools, our mission is to help advance the quality, the affordability, and the accessibility of education. At our current pace, we will facilitate $100 billion in charitable giving over the next decade–enough money to send more than 1 million students to college, tuition-free.
GiveCampus is backed by leading investors including Y Combinator, but we’re also practitioners of Sustainable Growth: we’ve made the Inc. 5000 list of America's fastest-growing private companies each of the last five years and we’ve been profitable nine of the last 10. In 2025, we celebrated a $140 million growth investment that included a major liquidity event for GiveCampus employees–the second in less than three years.
Our purpose-driven team of 130+ is located in 30+ states across the US: team members work from anywhere they choose. We have a beautiful 12,000sf office in Washington, DC that is available for people to use whenever they want, and we regularly organize team meet-ups, visit partner institutions, and host retreats in various locations.
While we operate at meaningful scale, we’re still small relative to the commercial and social good opportunities in front of us. Every GiveCampus employee plays a meaningful role in shaping what comes next, and we're growing the team in support of our ambitious plans–including a $100 million investment in AI product development. If you believe in the transformative power of education and want to join a fast-growing, mission-driven company, you’ll fit right in.
Location: This is a remote-first role based in the U.S. While we embrace flexible, distributed work, we also value in-person connection. Team members are expected to attend multiple company-wide and team-specific onsites throughout the year.
We're looking for a Senior ML Engineer to own the productionization and operational lifecycle of our machine learning models. You'll work closely with our Data Scientist, who focuses on customer discovery and prototype development, to take validated models from notebooks to production systems that serve predictions to our customers.
This is our first ML Engineer position, and you will be instrumental in defining the direction of our ML Platform. This is a high-impact role where you'll shape how we build and operate ML systems. You'll be responsible for the full journey from prototype handoff through deployment, monitoring, and ongoing maintenance. Over time, you'll build reusable tooling and self-service capabilities that enable faster iteration between Data Science and Production—reducing handoff friction and accelerating time-to-value for new models.
Responsibilities will include:Model Productionization
- Transform non-production prototypes (e.g. Jupyter notebooks, standalone scripts, etc.) into modular, tested, production-ready Python code
- Containerize models with proper dependency management (Docker, ECR)
- Implement comprehensive testing: unit tests, integration tests, model validation
Pipeline Development
- Build automated training pipelines using SageMaker Pipelines and Step Functions
- Develop batch and real-time inference pipelines based on use case requirements
- Integrate with Snowflake for feature retrieval and prediction storage
Deployment & Serving
- Deploy models to SageMaker endpoints for real-time inference
- Configure batch transform jobs for bulk predictions
- Integrate predictions with our Rails application via APIs and webhooks
Operations & Maintenance
- Monitor model performance, latency, and drift in production
- Build automated retraining pipelines triggered by schedule or drift detection
- Own incident response for ML systems—you're on the hook when models break
- Optimize costs across compute, storage, and inference
Platform & Tooling
- Build reusable templates, libraries, and tooling that accelerate future model deployments
- Create self-service capabilities that enable Data Science to deploy and test models with minimal friction
- Document patterns, runbooks, and best practices for ML operations
- 5+ years of software engineering experience, with 3+ years focused on ML systems
- Strong Python skills with emphasis on production code quality (not just notebooks)
- Experience deploying and operating ML models in production environments
- Hands-on experience with AWS (SageMaker preferred, but strong AWS fundamentals work)
- Proficiency with Docker and containerization best practices
- Understanding of ML concepts sufficient to work effectively with Data Scientists
- Experience building data pipelines and working with data warehouses (Snowflake a plus)
- Experience with SageMaker Pipelines, Feature Store, Model Registry
- Familiarity with Step Functions, EventBridge, or similar orchestration tools
- Infrastructure as Code experience (Terraform, CDK, CloudFormation)
- Experience with LLMs, RAG architectures, or generative AI applications
- Experience integrating ML systems with web applications (Rails, APIs)
- Background in B2B SaaS or EdTech
- ML Platform: AWS SageMaker (training, registry, endpoints)
- Data: Snowflake (single source of truth for model inputs)
- Orchestration: Step Functions, EventBridge
- Application: Rails (primary backend)
- Infrastructure: AWS, Terraform
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At GiveCampus, we value diversity and we pledge to foster an environment of support, inclusivity, and learning, both on the job and throughout the application process. In this spirit, we encourage candidates of all backgrounds to apply.
GiveCampus is an Equal Opportunity Employer. Applicants and employees are not discriminated against because of race, color, creed, sex, sexual orientation, gender identity or expression, age, religion, national origin, citizenship status, disability, ancestry, marital status, veteran status, medical condition or any protected category prohibited by local, state or federal laws.
If you feel like you don't meet all of the requirements for this role, please apply anyways. We know confidence gaps and imposter syndrome often get in the way of connecting with incredible people, and we don't want them to prevent us from meeting you.
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