Role overview
Deliver scalable production machine learning services to solve liquidity challenges. Build the next generation of payment applications that enable banks to settle cross-border payments through applying data engineering, machine learning, and software engineering. Design and implement tools and processes for the entire machine learning lifecycle, including feature generation, model development, model deployment, model serving, and experimentation. Build platforms and tools to enable scalable, auditable, and maintainable machine learning services at Ripple. Prototype solutions for complex software problems and implement them for on premise, cloud deployments, and use cases. Analyze user needs to engineer software solutions, modify existing software, and improve software performance. Monitor computer applications functioning to ensure specifications are met. Apply technical skills to effectively troubleshoot and thoroughly resolve the root cause of defects. Continuously raise Ripple’s standard of engineering excellence by implementing and driving best practices for coding, testing, and deployment. Communicate and collaborate with other Ripple engineers and applied scientists to bring the benefits of machine learning solutions to our customers and improve Ripple’s products. Telecommuting permitted 100% - may live anywhere in the U.S.
MINIMUM REQUIREMENTS:
Must have a Master’s degree in Computer Science or a related field plus 1 year of experience in machine learning engineering.
What you'll work on
- Complete software development lifecycle;
- Developing on one or more cloud services (such as GCP or AWS);
- Translating business requirements into technical requirements to build out modularized software architecture;
- Authoring and reviewing software design and code;
- Building and improving production quality machine learning models for classification, regression, forecasting or natural language processing problems;
- Post deployment management, debugging and monitoring of machine learning models and data pipelines; and
- Software deployment or MLOps concepts in relation to model lifecycle management
What we're looking for
- The opportunity to build in a fast-paced start-up environment with experienced industry leaders
- A learning environment where you can dive deep into the latest technologies and make an impact. A professional development budget to support other modes of learning.
- Thrive in an environment where no matter what race, ethnicity, gender, origin, or culture they identify with, every employee is a respected, valued, and empowered part of the team.
- Ripple is Flexible First: you have the option to work from home, from our offices, or a combination of the two around our centers of gravity (15 global offices).
- Weekly all-company meeting - business updates and ask me anything >
- We come together for moments that matter which include team offsites, team bonding activities, happy hours and more!