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Role overview

A senior executive position leading AI and machine learning engineering for a company whose products touch critical workflows for businesses. The role unifies classical ML and generative AI into a coherent technical strategy while maturing platform capabilities. It calls for someone who combines deep engineering credibility with executive influence to make AI a durable advantage.

Responsibilities

• Lead, manage, and develop a broad AI/ML organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, fostering a culture of technical excellence and customer impact.
• Define and execute a unified AI/ML systems strategy that brings together classical ML, generative AI, risk modeling, and platform capabilities in support of broader product and business goals.
• Partner with senior leaders across Product, Engineering, Design, Data, Risk, Legal, Security, and business teams to identify where AI can create meaningful customer value and operational leverage.
• Translate business problems into end-to-end AI/ML systems with clear standards for evaluation, monitoring, observability, reliability, safety, governance, and long-term maintainability.
• Mature AI/ML platform capabilities, tooling, primitives, guardrails, and deployment patterns that make it easier for product and engineering teams to build and operate AI/ML systems safely and with greater autonomy.
• 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.

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.
• Deep technical expertise across AI/ML systems, including classical ML, generative AI and LLMs, statistical modeling, risk modeling, and production-scale deployment.
• Strong software engineering and systems background covering data, retrieval, evaluation, deployment, routing, monitoring, observability, feedback loops, and lifecycle management.
• Experience scaling high-performing technical organizations across ML Engineers, AI/ML Platform teams, Risk Data Scientists, and AI Scientists.
• Experience evolving ML teams toward a stronger software engineering and systems orientation with clear ownership for production AI/ML systems.
• Executive-level strategic judgment with the ability to shape company-wide AI/ML priorities and influence senior stakeholders.

Nice to have

• Advanced degree in computer science, machine learning, statistics, or a related field, paired with demonstrated systems leadership and executive-level impact.

Benefits and work setup

• Cash compensation is targeted at roughly $275,000–$305,000 in most remote locations and $320,000–$350,000 in major metropolitan hubs, with final offers determined by experience and expertise.
• Hybrid expectation of approximately 2–3 days per week in a physical office for employees based near an office location.
• Secure, reliable, and consistent internet connection required for remote work days.
• Full-time employees receive competitive base pay, benefits, and equity participation, with offer amounts determined by role, level, and location.