Software Engineer, ML Platform

Experteer Overview Join Gusto’s ML Platform team as a Machine Learning Platform Engineer to design and scale our ML/AI infrastructure. You will partner with AI/ML engineers to deploy reliable, scalable systems that support the ML lifecycle, data pipelines, and deployment workflows. Your work enables teams to build, test, and iterate models rapidly while embedding AI-powered guidance into engineering processes. This role offers impact at scale and the chance to shape how AI drives product decisions across the company. Compensation / Benefits * Build core components of the ML/AI platform roadmap and automate pipelines for model development, deployment, monitoring, and retraining * Develop and improve frameworks for ML model development and deployment * Collaborate with ML/AI builders and application owners to define requirements and SLAs for API-enabled services * Develop, maintain, and enhance infrastructure supporting ML services * Support new patterns for model deployment with CI/CD pipelines and automated testing * Apply AI tools in engineering workflows and promote an AI-native approach to decisions * Adopt best practices for using AI technologies across technical development Tasks * 5+ years of software engineering experience (Python, Ruby or Java) * Experience designing and building ML lifecycle infrastructure (feature stores, model development, deployment, observability tools) * Experience with at least one major cloud platform (AWS preferred) * Curiosity and experimentation with emerging AI frameworks and best practices for scaling AI * Comfort with AI-assisted development tools and staying current with new approaches Key requirements * competitive base pay * benefits * equity (RSUs)

pipelines and automated testing * Apply AI tools in engineering workflows and promote an AI-native approach to decisions * Adopt best practices for using AI technologies across technical development Tasks * 5+ years of software engineering experience (Python, Ruby or Java) * Experience designing and building ML lifecycle infrastructure (feature stores, model development, deployment, observability tools) * Experience with at least one major cloud platform (AWS preferred) * Curiosity and experimentation with emerging AI frameworks and best practices for scaling AI * Comfort with AI-assisted development tools and staying current with new approaches Key requirements * competitive base pay * benefits * equity (RSUs)