ML Platform Engineer — Scalable AI Infra + Equity

Gusto; Denver (CO); Hybrid; USD 160,000 - 240,000; Full time

Job summary

Gusto is seeking a Machine Learning Platform Engineer to join the ML Platform team in Denver. You will build, scale, and operate ML/AI infrastructure and pipelines that power our platform’s ML and AI capabilities, delivery automation, and data reliability across teams.

Collaborate with AI/ML engineers to define requirements, implement CI/CD for ML workloads, ensure observability and security in production, and promote an AI-native approach to engineering and product decisions.

Qualifications

• 5+ years of software engineering experience (Python, Ruby or Java).
• Experience designing infrastructure for ML lifecycle, such as feature stores and model deployment.
• Experience with at least one cloud platform (AWS preferred).
• Curiosity with emerging AI frameworks and safe AI scaling practices.
• Comfort with AI-assisted development tools and staying current with new approaches.

Responsibilities

• Build ML/AI platform components and MLOps pipelines to design, build, run, monitor, debug, and retrain models.
• Develop, maintain, and enhance frameworks for model development and deployment.
• Collaborate with ML/AI teams to determine business requirements and SLAs for API-enabled services.
• Develop, maintain, and enhance infrastructure supporting ML services.
• Support deployment patterns for ML models with CI/CD and automated testing.
• Apply AI tools to improve engineering workflows and surface proactive guidance.
• Adopt best practices for AI technologies across development.

Skills

Python

Ruby

Java

ML infrastructure

AWS

Tools

CI/CD pipelines

Observability tooling

Job description

Gusto is seeking a Machine Learning Platform Engineer to join the ML Platform team in Denver. You will build, scale, and operate ML/AI infrastructure and pipelines that power our platform’s ML and AI capabilities, delivery automation, and data reliability across teams.

Collaborate with AI/ML engineers to define requirements, implement CI/CD for ML workloads, ensure observability and security in production, and promote an AI-native approach to engineering and product decisions.