Job Description
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Company Overview
A leading technology company is focused on helping small businesses manage critical business operations, including payroll, health insurance, retirement plans, and human resources. Serving more than 500,000 small businesses nationwide, the organization uses technology, data, and AI to simplify complex workflows and help business owners focus on their customers and craft.
Key Responsibilities
- Lead, manage, and develop a broad AI/MLE organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists.
- Foster a culture of technical excellence, customer impact, collaboration, innovation, and continuous learning.
- Define and execute the organization’s AI/ML systems strategy across classical ML, Generative AI, risk modeling, and platform capabilities.
- Partner with senior leaders across Product, Engineering, Design, Data, Risk, Legal, Security, and business functions to identify opportunities where AI/ML can generate customer value and business impact.
- Shape the development of AI-native products and internal systems by establishing standards for evaluation, monitoring, observability, reliability, safety, governance, and maintainability.
- Lead the development and maturation of AI/ML platform capabilities, tooling, primitives, guardrails, and deployment patterns.
- Enable product and engineering teams to build, evaluate, deploy, and operate AI/ML systems with greater autonomy and reduced friction.
- Establish disciplined technical and business decision-making around AI/ML investments, including determining when to build, leverage existing capabilities, or avoid unnecessary complexity.
- Support rapid experimentation and learning while ensuring production systems meet strong standards for quality, reliability, operational rigor, and accountability.
- Establish clear goals, KPIs, and operating rhythms to measure AI/ML performance, adoption, and business impact.
- Communicate progress, risks, investment requirements, and strategic tradeoffs clearly to senior leadership.
- Stay current with advances in AI/ML and evaluate emerging technologies based on their practical value, durability, and readiness for production.
- Establish a strong technical direction and operating model for AI/ML across the organization.
Required Qualifications
- 10+ years of experience leading teams in applied machine learning, AI, engineering, or data science roles.
- Proven track record of delivering impactful customer-facing software solutions.
- Deep technical expertise across AI/ML systems, including classical machine learning, Generative AI/LLMs, statistical modeling, risk modeling, and production-scale deployment.
- Strong software engineering and systems expertise.
- Experience leading technical strategy across data, retrieval, evaluation, deployment, routing, monitoring, observability, feedback loops, and AI/ML lifecycle management.
- Experience leading and scaling high-performing technical organizations.
- Experience managing or working closely with Machine Learning Engineers, AI/ML Platform teams, Risk Data Scientists, and/or AI Scientists.
- Experience evolving ML teams toward stronger software engineering and systems-oriented practices.
- Strong platform engineering orientation, including experience building tools, primitives, guardrails, and self-service capabilities.
- Ability to establish clear ownership for building, operating, and continuously improving production AI/ML systems.
- Executive-level strategic judgment and the ability to shape company-wide AI/ML priorities.
- Ability to align senior leaders around technical and business tradeoffs and make clear investment decisions.
- Strong executive communication and influence skills, including the ability to explain complex AI/ML concepts, risks, tradeoffs, and investment needs.
- Experience partnering with senior leaders across product, engineering, design, data, risk, legal, security, and business functions.
- Strong understanding of how classical ML and Generative AI can work together within modern production environments.
- Understanding of modern AI platform capabilities, including retrieval, evaluation, agents, observability, and production lifecycle management.
- Ability to provide practical judgment and leadership in ambiguous, company-level opportunities.
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