Compensation Overview

About the Role

Gusto is seeking an experienced senior engineering leader to lead our ML & AI Platform team. This team owns the systems and tooling that powers our ML and AI applications, and enables Data Scientists, Applied ML, and Applied AI Scientists to efficiently build, test, deploy, and monitor models. 

As a key member of Gusto’s ML & AI leadership group, your role will encompass leading a dynamic team to build out a robust and scalable platform that accelerates ML and AI applications across the business and bring their benefits to SMBs. This is a highly impactful role in charge of advancing our foundational capabilities, and we’d be thrilled to hear from you!

About the Team

The ML & AI team is responsible for the infrastructure, systems, and tooling that Gusto uses to build, deploy and monitor ML and AI applications. The space is complex and continuously evolving, especially with the advent of AI, and this role offers the opportunity to drive the building of a platform that streamlines and accelerates business impact. 

Here’s what you’ll do day-to-day

• Guide critical parts of the team charter and strategy to build out our ML & AI Platform
• Collaborate with Data Scientists, Applied ML, and Applied AI teams to understand their needs, identify and prioritize initiatives to accelerate their ML/AI workflows
• Participate in technical design discussions and software delivery while growing the team
• Collaborate with engineering teams like Data Platform and Infrastructure to inform system design and to integrate the new systems and tools into Gusto’s stack
• Define and develop best practices and processes to support efficient team operations

Here’s what we’re looking for

• 8+ years of experience as a ML or Data Platform engineer, with 4+ years of experience in ML/AI
• 3+ years of engineering management experience ideally in a ML/AI platform role
• Expertise in building and supporting ML ops and data infrastructure, ideally in the cloud
• Exposure and interest in LLM Ops and Generative AI applications and infrastructure
• Strong technical acumen with the ability to understand and debate technical and product tradeoffs and approaches to building scalable architecture 
• Passion for developing, coaching, and scaling direct reports
• Experience with MLOps tooling such as KubeFlow, AWS Sagemaker, MLFlow, or other ML Ops tools

Our cash compensation amount for this role is targeted at $191,000-$237,000/year in Denver, Chicago, and Atlanta, $208,000-$258,000/year in Los Angeles, and $225,000-$279,000/year for Seattle, San Francisco and New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.