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.
Nice to Have
- 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.
Benefits
- Competitive base pay
- Benefits
- Equity (RSUs)
