ML Platform Engineer — Scale AI Pipelines

Gusto, Inc.; Denver (CO); Hybrid; USD 160,000 - 200,000; Full time

Benefits offered by this job

Equity (RSUs)

Benefits

Job summary

Gusto, Inc. is seeking a Machine Learning Platform Engineer to join the ML Platform team and scale our ML/AI platform.

You will work with AI/ML engineers to rapidly build, deploy, and iterate reliable ML infrastructure across the organization, enabling scalable model development and deployment. The ideal candidate has 5+ years in software engineering and a strong background in ML infrastructure, data pipelines, and cloud platforms.

Qualifications

• 5+ years of software engineering experience in Python, Ruby or Java.
• Experience designing infrastructure for ML lifecycle (feature stores, model deployment, observability).
• Experience with at least one major cloud platform (AWS preferred).
• Curiosity and experimentation with emerging AI frameworks, applying best practices to scale AI use.
• Comfort with AI-assisted development tools and staying current with AI approaches.

Responsibilities

• Build core components of the ML/AI platform roadmap and design MLOps pipelines.
• Develop, maintain, and enhance frameworks for ML model development and deployment.
• Collaborate with ML/AI builders and stakeholders to define requirements and SLAs for API services.
• Develop and maintain infrastructure supporting ML services and pipelines.
• Support deployment patterns for ML models with CI/CD and automated testing.
• Apply AI tools to engineering workflows and promote AI-native practices.

Skills

Python

Ruby

Java

ML Platform Design

Tools

AWS

GCP

Job description

Gusto, Inc. is seeking a Machine Learning Platform Engineer to join the ML Platform team and scale our ML/AI platform.

You will work with AI/ML engineers to rapidly build, deploy, and iterate reliable ML infrastructure across the organization, enabling scalable model development and deployment. The ideal candidate has 5+ years in software engineering and a strong background in ML infrastructure, data pipelines, and cloud platforms.