Director, AI
New
I
iSpotAdvertising Technology
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.Full-TimeDirector
Salary$204,507 - $269,942 USD Annually
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Job Details
- Experience
- 10+ years of software engineering experience, including 3+ years leading engineering teams.
- Required Skills
- Machine LearningRESTful APIsSoftware EngineeringLLMMLOpsGenerative AIDistributed Systems
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.
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.
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