Give Campus
Senior Machine Learning Engineer
🇺🇸
Remote - US
🕑
Full-Time
💰
TBD
💻
Software Engineering
🗓️
February 6th, 2026
Docker
Jupyter
K-12
Edtech.com's Summary
GiveCampus is hiring a Senior Machine Learning Engineer to lead the productionization and operational lifecycle of machine learning models. This role involves transforming prototypes into production-ready systems, building deployment pipelines, and maintaining model performance to support educational fundraising platforms.
Highlights
- Own end-to-end ML model production from prototype to deployment and maintenance
- Develop automated training and inference pipelines using AWS SageMaker and Step Functions
- Deploy models to SageMaker endpoints and integrate predictions with Rails applications via APIs
- Monitor model performance, detect drift, and implement automated retraining
- Create reusable tooling and self-service capabilities for faster ML iteration
- Strong Python programming skills focused on production-quality code
- Experience with AWS cloud services, Docker containerization, and Snowflake data warehouses
- Required qualifications include 5+ years software engineering with 3+ years in ML systems
- Bonus skills: SageMaker Pipelines, Terraform, generative AI, and B2B SaaS or EdTech experience
- Technology stack includes AWS SageMaker, Snowflake, Step Functions, Rails, and Terraform
Senior Machine Learning Engineer Full Description
GiveCampus is the world's leading fundraising platform for non-profit educational institutions. Trusted by 1,300+ colleges, universities, and K-12 schools, our mission is to help advance the quality, the affordability, and the accessibility of education. We received a seed investment from Y Combinator in 2015 and have pursued a strategy of 'Sustainable Growth' ever since: achieving six consecutive years of profitability and positive cash-flow while more than quadrupling our revenue, our customer base, and our team. In 2022, we raised $50 million to accelerate the next stage of our growth.
Through The GiveCampus Social Mobility Initiative, we've donated $1 million in free fundraising support for programs that help low-income students, first-generation students, and underrepresented minorities. And in 2022 and 2023, we were named to Y Combinator's Top Companies list and the Inc. 5000 list of America's fastest-growing private companies.
While we operate at meaningful scale (we've facilitated more than $6 billion in charitable giving), we’re still small relative to the commercial and social good opportunities in front of us. Every GiveCampus employee has a substantial impact on our trajectory, and we're growing to help schools achieve even greater results.
Our purpose-driven team of 120+ is located across the US: team members work from anywhere they choose. We have a beautiful 12,000 sf office in Washington, DC that is available for people to use whenever they want, and we regularly organize team meet-ups, events, and retreats in various locations. We're looking to expand our team with diverse and collaborative doers who believe in our mission and the transformative power of affordable, high-quality education.
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.
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
What we are looking for:
- 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)
Bonus points if you have:
- 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
Our Tech Stack
- 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
Ready to apply?
Be sure to keep an eye on your spam and promotions boxes in case our emails end up there!
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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