Gusto is looking to build, deploy, and iterate high-quality ML infrastructure solutions at scale to support its growing business.
Requirements
At least 10 years of software engineering experience (Python, Ruby or Java).
Demonstrated experience architecting and developing infrastructure and platform services for machine learning lifecycle, such as feature stores, model development, deployment, and observability tools and solutions.
Experience with at least one of the major cloud platforms (AWS preferred but not required).
Experience with MLOps tooling such as KubeFlow, AWS Sagemaker, MlFlow, or similar.
Responsibilities
Drive core components of our ML Platform technical roadmap to design and build MLOps solutions with automated pipelines and standardized processes to build, deploy, run, monitor, debug, and retrain ML Models.
Develop, maintain, and enhance frameworks for machine learning model development and deployment.
Collaborate with the ML model builders and application owners to determine business requirements and SLAs for API-enabled services.
Develop, maintain, and enhance infrastructure supporting machine learning services.
Support the development of new patterns for the deployment of machine learning models with CI/CD pipelines and automated testing.
Other
Work from the office on designated days approximately 2-3 days per week (or more depending on role).
A secure, reliable, and consistent internet connection is required when working from a location other than a Gusto office.
