Director, AI - iSpot

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Applicants must be currently authorized to work in the United States. iSpot is not able to sponsor or take over sponsorship of an employment visa for this position at this time.

iSpot competes for the best talent. Our compensation packages consist of salary and equity in one of Seattle's hottest start-ups, as well as other standard benefits. Most importantly, we provide a really interesting working experience, and the chance to contribute to the success of something great.

At iSpot, we are transforming how brands, agencies, publishers, and platforms measure advertising outcomes across linear, streaming, and digital ecosystems. Artificial intelligence is a critical part of our product strategy and future growth.

As Director of AI, you will lead the team that builds and ships iSpot's AI-powered capabilities across our Creative, Audience, and Outcomes (CAO) product lines. You will take AI concepts from prototype to production — intelligent creative analysis, audience insights, agentic workflows, and next-generation outcome measurement — and make them reliable, evaluated, and economical at scale.

This role sits at the intersection of engineering, product, and data science. You will partner closely with Product Management, Data Science, and Platform teams to sequence the work, resolve dependencies, and deliver AI features customers actually adopt.

This is a hands-on leadership role. You will lead and grow a team, but we expect you to prototype, review architectures, write code, evaluate emerging models, and work directly alongside engineers to turn ideas into customer value.

• Lead the day-to-day execution of iSpot's AI engineering roadmap across the Creative, Audience, and Outcomes (CAO) product portfolio.
• Build, ship, and operate customer-facing AI capabilities — including generative AI features, agentic workflows, retrieval systems, and applied machine learning.
• Own the architecture and technical patterns for AI services: model selection, orchestration, evaluation, guardrails, cost, and latency.
• Prototype quickly and personally — write code, run experiments, and validate concepts before committing the team to a direction.
• Hire, lead, and develop a team of AI and machine learning engineers, setting a high bar for engineering craft and delivery.
• Partner with Product Management to turn opportunities into scoped, sequenced roadmaps with clear success criteria.
• Partner with Data Science to product ionize models and measurement methodology at scale.
• Establish evaluation and experimentation frameworks that make AI quality measurable and regressions visible before customers see them.
• Instrument and manage the economics of AI systems — inference cost, throughput, caching, and capacity planning.