Sr. Machine Learning Engineer

What we do

Founded in 2015, ReUp Education is the only organization that focuses exclusively on helping colleges and universities engage and re-enroll the more than 40 million US residents who have “stopped out” and support them until graduation, through our technology-enabled service. To date, we have re-enrolled nearly 28,500 students and recaptured over $139 million in tuition for our university partners. Today we build regional marketplaces to connect the supply of educational opportunities with the demand for skilled and credentialed workforce professionals.

About the role

• ReUp Education is seeking an experienced Sr Machine Learning Engineer to join our team to support institutions of higher learning and their millions of adult learners. This role will be a key member of the engineering team that supports the foundational infrastructure and data that drives the various tools and products within ReUp Education, as well as provide the foundation for insights, predictions, and trends that stem from the data Reup collects from its partners.
• As a member of the Engineering team, you will be a hands-on contributor who will own the full data model lifecycle. You will partner with data scientists, data analysts, and data engineers to provide resilient, scalable services that integrate directly with our data warehouse and power data insights and predictions.
• As a Sr Machine Learning Engineer, you will collaborate cross-functionally with teams across the organization to understand how ingested data is being utilized, identify data gaps or missing information, and troubleshoot any anomalies. Additionally, you will provide technical leadership alongside the engineering leadership team, ensuring best practices are followed. A strong background in AWS, training data models, understanding deployment techniques, ETL processes, database management, and reporting systems is essential to drive scalable, efficient data solutions.
• If you are passionate about working on a project that will have a real-world positive impact on people's day-to-day lives, we encourage you to apply!

What you'll do

As a Sr. Machine Learning Engineer, you will be working with the core information that drives our tools and products. Your role involves but are not limited to:

• Design, train, and tune data models based on cross-departmental requirements for a variety of applications and reporting requirements.
• Automate retraining of data models to ensure up-to-date statistics and analysis.
• Build production-grade pipelines, containerize models (Docker/Kubernetes/ECS), and establish strong CI/CD practices .
• Design, build, and maintain robust and scalable data pipelines for collecting, transforming, storing, and delivering large datasets from Redshift and other data sources.
• Design and implement data architectures that support analytics and reporting needs, ensuring efficient data storage and retrieval.
• Optimize models, database performance, and data processing jobs to minimize latency and improve efficiency.
• Continuously monitor and tune data processing pipelines, data modeling, databases, and queries to enhance performance, reduce latency, and minimize costs.
• Implement robust security measures to ensure the confidentiality and integrity of sensitive data, including encryption, access controls, and data masking.
• Maintain comprehensive documentation for data models, workflows, pipelines, infrastructure and facilitate knowledge sharing and team collaboration.

Qualifications

Research shows that women and people from underrepresented groups often only apply to jobs if they meet all of the qualifications. However, no one ever meets 100% of the qualifications. ReUp encourages you to break that statistic and to apply. We look forward to your application.

We are looking for a seasoned Sr. Machine Learning Engineer with a proven track record. 

Ideally, you have:

• 5+ years in Machine Learning, Data Science, Data Engineering or a related field.
• Expert Python or similar skills with a focus on tools like scikit-learn, TensorFlow or PyTorch.
• Package models in Docker containers and deployment via AWS Elastic Bean