Head of AI and Machine Learning Engineering

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.