Ripple - San Francisco, CA
posted 14 days ago
About the position
We are looking for a Staff Software Engineer, Machine Learning to optimize the way Ripple interacts with digital asset markets. Candidates will partner closely with applied scientists to deliver scalable production machine learning services to solve these liquidity challenges. You will be responsible for the implementation of the infrastructure and tools to build and deploy production models, as well as contributing back to our greater machine learning platform. You will know how the rubber meets the road in deploying models to production, working with real-time data and implementing critical checks. This will ensure we tackle business problems in as rigorously as we tackle research problems; and that we use models as scientifically as we develop them. Ideal candidates will have a track record of technical excellence in crafting, building and delivering reliable solutions as part of a team. As a member of a growing initiative, you must be passionate about inventing and delivering customer-focused solutions to ambitious and ambiguous challenges. You will chip in and maintain production-level code, improving for engineering standards. You can clearly identify feature requirements for solutions, even when problem statements are ambiguous. You are adept at presenting across teams to drive multi-functional alignment and have the ability to think through how markets will adapt to leverage paradigm-shifting technology.
Responsibilities
- 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 ML 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.
Requirements
- Degree or equivalent experience in computer science or other quantitative field; masters or PhD preferred
- 8+ years software engineering experience with 5+ 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
