Senior Machine Learning Engineer

GiveCampus

4h•Remote

About The Position

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. 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.

Requirements

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)

Nice To Haves

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

Responsibilities

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
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
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
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
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