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Senior Machine Learning Engineer - Platform
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Gusto
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About Gusto Gusto is a modern, online people platform that helps small businesses take care of their teams. On top of full-service payroll, Gusto offers health insurance, 401(k)s, expert HR, and team management tools. Today, Gusto offices in Denver, San Francisco, and New York serve more than 400,000 businesses nationwide.
About The Role As a Senior 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, data pipelines, and data infrastructure will be crucial in developing a dependable and scalable ML infrastructure for all of Gusto.
The ideal candidate is passionate about developing software, documenting processes, working with data, and understanding end-user needs. A strong grasp of ML and data infrastructure is essential to build efficient solutions that enable our partners to scale significantly.
Responsibilities
Drive core components of our ML Platform technical roadmap to design and build MLOps solutions with automated pipelines and standardized processes for deployment, monitoring, debugging, and retraining ML models.
Develop, maintain, and enhance frameworks for machine learning model development and deployment.
Collaborate with model builders and application owners to define business requirements and SLAs for API-enabled services.
Support infrastructure development supporting machine learning services.
Develop new deployment patterns for ML models with CI/CD pipelines and automated testing.
Qualifications
At least 10 years of software engineering experience in Python, Ruby, or Java.
Experience architecting and developing infrastructure and platform services for ML lifecycle management, such as feature stores, deployment, and observability tools.
Experience with at least one major cloud platform (AWS preferred).
Experience with MLOps tools like KubeFlow, AWS Sagemaker, MlFlow, or similar.
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Senior Machine Learning Engineer - Platform
role at
Gusto
Get AI-powered advice on this job and more exclusive features.
About Gusto Gusto is a modern, online people platform that helps small businesses take care of their teams. On top of full-service payroll, Gusto offers health insurance, 401(k)s, expert HR, and team management tools. Today, Gusto offices in Denver, San Francisco, and New York serve more than 400,000 businesses nationwide.
About The Role As a Senior 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, data pipelines, and data infrastructure will be crucial in developing a dependable and scalable ML infrastructure for all of Gusto.
The ideal candidate is passionate about developing software, documenting processes, working with data, and understanding end-user needs. A strong grasp of ML and data infrastructure is essential to build efficient solutions that enable our partners to scale significantly.
Responsibilities
Drive core components of our ML Platform technical roadmap to design and build MLOps solutions with automated pipelines and standardized processes for deployment, monitoring, debugging, and retraining ML models.
Develop, maintain, and enhance frameworks for machine learning model development and deployment.
Collaborate with model builders and application owners to define business requirements and SLAs for API-enabled services.
Support infrastructure development supporting machine learning services.
Develop new deployment patterns for ML models with CI/CD pipelines and automated testing.
Qualifications
At least 10 years of software engineering experience in Python, Ruby, or Java.
Experience architecting and developing infrastructure and platform services for ML lifecycle management, such as feature stores, deployment, and observability tools.
Experience with at least one major cloud platform (AWS preferred).
Experience with MLOps tools like KubeFlow, AWS Sagemaker, MlFlow, or similar.
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