Asks for

The ad says“Working knowledge of modern AI technologies: large language models, agentic systems, retrieval architectures, embeddings, evaluation methods, and fine-tuning trade-offs.”
The ad says“Practical understanding of AI safety, data governance, privacy, and enterprise security requirements.”
The ad says“Strong software engineering fundamentals — distributed systems, APIs, data pipelines, testing, and production operations.”
The ad says“Practical understanding of AI safety, data governance, privacy, and enterprise security requirements.”
The ad says“Strong software engineering fundamentals — distributed systems, APIs, data pipelines, testing, and production operations.”
The ad says“Strong software engineering fundamentals — distributed systems, APIs, data pipelines, testing, and production operations.”
The ad says“Working knowledge of modern AI technologies: large language models, agentic systems, retrieval architectures, embeddings, evaluation methods, and fine-tuning trade-offs.”
The ad says“Practical understanding of AI safety, data governance, privacy, and enterprise security requirements.”
The ad says“Working knowledge of modern AI technologies: large language models, agentic systems, retrieval architectures, embeddings, evaluation methods, and fine-tuning trade-offs.”
The ad says“Working knowledge of modern AI technologies: large language models, agentic systems, retrieval architectures, embeddings, evaluation methods, and fine-tuning trade-offs.”
The ad says“Practical understanding of AI safety, data governance, privacy, and enterprise security requirements.”
The ad says“Strong software engineering fundamentals — distributed systems, APIs, data pipelines, testing, and production operations.”
The ad says“Working knowledge of modern AI technologies: large language models, agentic systems, retrieval architectures, embeddings, evaluation methods, and fine-tuning trade-offs.”
The ad says“Strong software engineering fundamentals — distributed systems, APIs, data pipelines, testing, and production operations.”
The ad says“Strong software engineering fundamentals — distributed systems, APIs, data pipelines, testing, and production operations.”

Immigration / Work Authorization Notice: 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.

What You’ll Be Part Of:

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.

Responsibilities:

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 productionize 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.
Evaluate emerging models, tools, and vendors, and make pragmatic build-versus-buy recommendations.
Embed responsible AI practices, data governance, privacy, and security into every customer-facing solution.
Report progress, risks, and results clearly to engineering and executive leadership.

Qualifications and Education Requirements:

Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field; advanced degree preferred.
10+ years of software engineering experience, including 3+ years leading engineering teams.
Demonstrated experience shipping AI or machine learning capabilities into production products used by customers.
Working knowledge of modern AI technologies: large language models, agentic systems, retrieval architectures, embeddings, evaluation methods, and fine-tuning trade-offs.
Strong software engineering fundamentals — distributed systems, APIs, data pipelines, testing, and production operations.
Genuinely hands-on: comfortable in the codebase, in notebooks, and in production traces.
Experience partnering across product management, data science, and platform engineering.
Clear communicator who can explain technical trade-offs to non-technical stakeholders.
Track record of delivering in fast-paced environments with shifting priorities.
Practical understanding of AI safety, data governance, privacy, and enterprise security requirements.

Preferred Skills:

Experience within advertising technology, media measurement, marketing technology, or consumer insights platforms.
Experience building and operating agentic AI systems or autonomous workflows in production.
Familiarity with modern MLOps practices, LLM observability, and cloud-native AI platforms.
Experience with large-scale data processing and analytics infrastructure.
Experience working directly with enterprise customers to shape and validate AI capabilities.
MS or advanced technical degree preferred.

Additional Notes:

This role is focused on execution — building, shipping, and operating the AI capabilities that differentiate iSpot's Creative, Audience, and Outcomes product portfolio.
Success will be measured by shipped capabilities, adoption and quality of AI features in production, the reliability and cost efficiency of AI systems, and the strength of the team you build.
We are looking for a builder and technical leader who combines strong engineering judgment with a bias for experimentation and rapid learning. The ideal candidate is equally comfortable framing an approach for executives and pairing with an engineer to get it working.

Target cash compensation range: $204,507 - $269,942 USD Annually

We are committed to providing competitive, market-informed compensation. The cash compensation above includes base salary, variable commission for employees in eligible roles, and annual bonus targets for eligible roles. In addition to cash compensation, all full time iSpotters are eligible to participate in iSpot’s equity plan to receive stock options. Non-exempt roles will also be eligible for (pre-approved) overtime pay. Individual compensation packages are influenced by different factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons.

For more information on total rewards package, go HERE

Hybrid & Flexible Workplace Policy

iSpot supports a hybrid and flexible workplace. Depending on location and work responsibilities, employees may be designated as full-time or part-time office-based or a fully remote employee. A hybrid work schedule indicates that you work in the office some days and work from home other days. The best hybrid workplaces allow for flexibility while also encouraging consistency.

Those local or living in surrounding areas to one of our offices (Bellevue, WA or New York, NY) will work a hybrid schedule, coming into their local office 1-3 days a week. While those in a role, not office-based and located further away from our offices, will work a fully remote schedule. If you have questions regarding exact details of our hybrid & flexible workplace policy, please let your recruiter know and they will discuss with you further.

#LI-Hybrid

If you don't feel you met every single requirement for the role, don't rule yourself out. Please apply anyway!

iSpot is an equal opportunity employer. All applicants will receive consideration for employment without regard to race, ethnicity, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please contact our HR team.

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