Head of AI and Machine Learning Engineering

About Gusto At Gusto, we’re on a mission to grow the small business economy. We handle the hard stuff — payroll, health insurance, 401(k)s, and HR — so owners can focus on their craft and their customers. With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.

All full-time employees receive competitive base pay, benefits, and equity (RSUs) — because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy.

AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.

About The Role Gusto sits at the center of many of the most important workflows for small businesses, which creates a meaningful opportunity to use rich product and customer data to build AI- and ML-powered systems that improve customer experiences, automate complex work, support better decision-making, and help small businesses thrive. As Gusto becomes more AI-native, we are evolving how AI, ML, risk modeling, and platform capabilities come together across our products and internal systems.

We are seeking a strategic Head of AI/MLE to lead this next chapter. In this key leadership role, you will define how Gusto builds, deploys, evaluates, and scales AI/ML systems across the company. You will lead a broad organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, while partnering closely with senior business leaders to shape where AI can create durable customer and business impact.

As the Head of AI/MLE at Gusto, you will be responsible for unifying classical ML and GenAI into a coherent technical strategy, maturing the platform for broader self-service adoption, and shaping how AI-native products and production systems are built at Gusto. Your teams will help translate business problems into end-to-end AI/ML systems, from experimentation and prototyping through evaluation, deployment, monitoring, feedback loops, and operational governance.

Your leadership will be critical in setting the technical direction, operating model, and quality bar for AI at Gusto. You will help teams move quickly where speed and learning matter most, while ensuring production systems meet high standards for reliability, measurement, safety, and long-term maintainability. This is a senior technical executive role for someone who can combine deep engineering credibility, strong business judgment, and executive-level influence to make AI a durable advantage for Gusto.

Here’s What You’ll Do Day-to-day

• Lead, manage, and develop a broad AI/MLE organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, fostering a culture of technical excellence, customer impact, collaboration, and continuous learning.
• Define and execute Gusto’s AI/ML systems strategy, unifying classical ML, GenAI, risk modeling, and platform capabilities into a coherent approach that supports Gusto’s broader business and product goals.
• Partner with senior leaders across Product, Engineering, Design, Data, Risk, Legal, Security, and business teams to identify where AI/ML can create meaningful customer value, business impact, and operational leverage.
• Shape how AI-native products and internal systems are built at Gusto, helping teams translate business problems into end-to-end AI/ML systems with clear standards for evaluation, monitoring, observability, reliability, safety, governance, and long-term maintainability.
• Lead the development and maturation of AI/ML platform capabilities, tooling, primitives, guardrails, and deployment patterns that make it easier for product and engineering teams to build, evaluate, deploy, and operate AI/ML systems with less friction, more autonomy, and the right quality bar.
• Drive disciplined technical and business judgment around AI/ML investments, including where to build, where to leverage existing capabilities, and where to avoid unnecessary complexity.
• Create room for fast experimentation and learning where appropriate, while ensuring high-impact production systems meet strong standards for…