Staff Machine Learning Engineer - Platform

About the Role:

As a Staff Machine Learning Engineer, you will work closely with applied science practitioners and engineers to rapidly build, deploy, and iterate high-quality ML infrastructure solutions at scale, ensuring both reliability and effectiveness. Your deep expertise in the machine learning

Model development cycle, along with a strong understanding of data pipelines and data infrastructure will be crucial in developing a dependable and scalable ML infrastructure for all of Gusto to rely on.

The ideal candidate is passionate about developing software, developing and documenting optimal processes, working with data, and understanding the needs of end users. A strong grasp of ML and data infrastructure is essential, as you will work with stakeholders to build efficient solutions to help our partners scale x times better.

Here’s what you’ll do day-to-day:

• 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.

Here’s what we're looking for:

• 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.