Senior Machine Learning Engineer

About GiveCampus

GiveCampus is the world's leading fundraising platform for non-profit educational institutions, trusted by millions of donors and over 1,300 colleges, universities, and K-12 schools. Our mission is to advance the quality, affordability, and accessibility of education. We aim to facilitate $100 billion in charitable giving over the next decade. Backed by Y Combinator and profitable for nine of the last ten years, GiveCampus has been on the Inc. 5000 list for five consecutive years. We recently celebrated a $140 million growth investment, including a major liquidity event for employees. Our purpose-driven team of 130+ is distributed across 30+ US states, with a primary office in Washington, DC. We are investing $100 million in AI product development and are looking for individuals who believe in the transformative power of education.

About the Role

This is our first ML Engineer position at GiveCampus, offering a high-impact opportunity to define the direction of our ML Platform. You will be instrumental in shaping how we build and operate ML systems. You will work closely with our Data Scientist to take validated models from notebooks to production systems, responsible for the full journey from prototype handoff through deployment, monitoring, and ongoing maintenance. Over time, you will build reusable tooling and self-service capabilities to enable faster iteration and accelerate time-to-value for new models.

Location

This is a remote-first role based in the U.S. While we embrace flexible, distributed work, team members are expected to attend multiple company-wide and team-specific onsites throughout the year.

Responsibilities

Model Productionization

• Transform non-production prototypes (e.g., Jupyter notebooks, standalone scripts) 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.
• 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.
• 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