Software Engineer, ML Platform

About Gusto

At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff — payroll, health insurance, 401(k)s, and HR — so owners can focus on their craft and their customers. With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.

All full-time employees receive competitive base pay, benefits, and equity (RSUs) — because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about ourTotal Rewards philosophy.

AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.

About the Role:

Gusto is looking for a strong Machine Learning Platform and Infrastructure Engineer to join our ML Platform team and build out and scale our ML and AI platform.

As a Machine Learning Platform Engineer, you will work closely with AI/ML engineers to rapidly build, deploy, and iterate high-quality ML/AI 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/AI 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:

• Build core components of our ML and AI Platform technical roadmap to design and build MLOps solutions with automated pipelines and standardized processes to build, deploy, run, monitor, debug, and retrain ML and AI Models.
• Develop, maintain, and enhance frameworks for machine learning model development and deployment.
• Collaborate with the ML/AI 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.
• Apply AI tools as a regula