Role overview
At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.
If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.
At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.
What you'll work on
- Be a bold builder, working up and down the data stack, mixing data engineering, machine learning and software engineering to jumpstart this new initiative.
- Build the platform and tools to enable scalable, auditable, and maintainable machine learning services at Ripple
- Design and implement tools and processes for the entire AI lifecycle including feature generation, model development, model deployment, model serving and experimentation
- Collaborate closely with other Ripple engineers and applied scientists, bringing the benefits of automation and machine learning solutions to our customers
What we're looking for
- Degree in computer science or other quantitative field; masters or PhD preferred
- 5+ years software engineering experience with 3+ years developing production machine learning systems
- Experience with Python and Java
- Experience with frameworks like scikit-learn, TensorFlow, or PyTorch
- Experience with machine learning lifecycle platforms (KubeFlow,MLFlow) and cloud data services (GCP, AWS, Databricks)
- Experience with real-time data a huge plus
- A collaborative coder, comfortable with Git and code reviews
- Excellent written and verbal communication skills
- Attention to detail and a dedication to excellence