gusto
Software Engineer, ML Platform
At a Glance
- Location
- Denver, CO, United States; New York, New York, United States; San Francisco, CA, United States
- Experience
- 5+ years
- Compensation
- for this role is targeted at $160,000-$200,000/year in Denver, and $190,000-
- Posted
- 2026-08-10T16:11:22-04:00
Key Requirements
Required Skills
Domain Knowledge
- Engineering
- Medical
Benefits & Perks
rd stuff — payroll, health insurance, 401(k)s, and HR — so owners can focus
Requirements
At least 5+ years of software engineering experience (Python, Ruby or Java).
Demonstrated experience designing 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).
Curiosity and experimentation with emerging AI frameworks
, applying and sharing best practices to evaluate and scale AI use safely across teams
Comfort with AI-assisted development tools and a habit of staying current with emerging approaches to building software.
Responsibilities
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.
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.
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.