Head of ML/AI Engineering

Gusto•San Francisco, NY

15h•Hybrid

About The Position

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.

Requirements

10+ years of experience leading teams in applied machine learning, AI, engineering, or data science roles, with a track record of delivering impactful customer-facing software solutions.
Deep technical expertise across AI/ML systems, including classical ML, GenAI/LLMs, statistical modeling, risk modeling, and production-scale deployment.
Strong software engineering and systems background, with the ability to lead technical strategy across data, retrieval, evaluation, deployment, routing, monitoring, observability, feedback loops, and lifecycle management.
Experience leading and scaling high-performing technical organizations, including Machine Learning Engineers, AI/ML Platform teams, Risk Data Scientists, and/or AI Scientists.
Experience evolving ML teams toward a stronger software engineering and systems orientation, with clear ownership for building, operating, and improving production AI/ML systems.
Strong platform orientation, with experience building tools, primitives, guardrails, and self-service capabilities that help product and engineering teams build AI/ML-powered products safely and effectively.
Executive-level strategic judgment, with the ability to shape company-level AI/ML priorities, align senior leaders around tradeoffs, and make clear investment decisions based on customer value, business impact, technical feasibility, risk, data readiness, and operational complexity.
Strong executive communication and influence, with the ability to explain complex AI/ML concepts and technical decisions in a way that clarifies strategy, tradeoffs, risk, investment needs, and organizational implications.
Experience operating as a peer to senior cross-functional leaders across product, engineering, design, data, risk, legal, security, and business teams — bringing clarity, urgency, and practical judgment to ambiguous company-level opportunities.
A clear thesis on how classical ML and GenAI should work together, how modern AI platform capabilities like retrieval, evaluation, agents, and observability should come together, and how AI/ML teams should evolve as the field becomes more software- and systems-oriented.

Nice To Haves

Experience in fintech, risk modeling, regulated environments, or domains with high standards for reliability, trust, and compliance is a plus.
Advanced degree in computer science, data science, machine learning, statistics, or a related field is a plus, but demonstrated systems leadership, production judgment, and executive-level impact matter most.

Responsibilities

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 quality, operational rigor, and business accountability.
Set clear goals, KPIs, and operating rhythms to measure the performance, adoption, and business impact of AI/ML systems, and communicate progress and tradeoffs clearly to senior leadership.
Stay close to the frontier of AI/ML advancement and help Gusto apply new technologies pragmatically, with strong judgment about what is durable, useful, and ready for production.