# Head of ML/AI Engineering **Company**: Gusto **Location**: Denver, CO;San Francisco, CA;New York, NY **Work arrangement**: remote **Experience**: senior **Job type**: full-time **Salary**: Competitive base pay, benefits, and equity (RSUs) **Category**: Engineering **Industry**: Technology **Apply**: https://job-boards.greenhouse.io/gusto/jobs/7948318?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_c79c8732-5fe ## Description 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. We're looking for a Head of ML/AI Engineering to lead our AI/ML efforts and help us build AI-native products and internal systems. 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. 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. 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. - 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. Our cash compensation amount for this role is targeted at $245,000-272,000 in Denver & most remote locations, and $288,000-321,000 for San Francisco & New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above. ## Skills ### Required - Machine Learning - Artificial Intelligence - Software Engineering - tell me about your experience with classical ML and GenAI - Risk Modeling - Statistical Modeling - Production-Scale Deployment - Data Retrieval - Evaluation - Deployment - Routing - Monitoring - Observability - Feedback Loops - Lifecycle Management - Platform Orientation - Tools - Primitives - Guardrails - Self-Service Capabilities - Executive-Level Strategic Judgment - Communication - Influence - Fintech - Regulated Environments - Reliability - Trust - Compliance --- Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/gusto/jobs/7948318?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)