adMarketplace
Senior Product Manager (AI/ML)
- Compensation
- $170K–$210K / yr
- Location
- New York, United States
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Ml / AI
- Posted
Skills
Full description
- We are looking for a Senior Product Manager to own search relevance, data architecture, and the data pipelines that power the next generation of native search ads across adMarketplace’s publisher and AI Chat surfaces
- This is a high-leverage, hands-on role with executive visibility
- You will sit at the intersection of Product, AI/ML, Supply, Product Analytics, and Supply Analytics, and you will be responsible for how we turn query understanding, feed quality, and relevance signals into measurable lift for consumers, publishers, and advertisers
- You will help define what native search advertising looks like on AI surfaces
- The work ranges from scoping the data platforms and feature stores that feed our models, to prototyping ranking and query-understanding ideas, to shipping them through a tight partnership with engineering and data science
- Own the product area. Own the roadmap for search relevance, data architecture, and data pipelines across native search and AI Chat surfaces, from ideation through launch and iteration
- Shape the data and ML foundation. Define, prioritize, and scope the data, feature, and feed investments that drive retrieval, ranking, and query understanding across the stack
- Lead on AI surfaces. Drive the product strategy for adMarketplace’s native ad presence on AI Chat surfaces, extending our first-to-market, first-to-scale position into durable advantage
- Prototype and technically scope. Write PRDs, build lightweight prototypes, and run technical scoping with ML engineers, data scientists, and data platform partners. You will be expected to read a schema, sketch a pipeline, and pressure test a model evaluation plan
- Partner across the org. Work directly with Product Analytics, Supply Analytics, AI/ML, and Supply to align on metrics, experimentation strategy, and the tradeoffs between relevance, yield, and publisher health
- Measure what matters. Define the metrics and evaluation frameworks that tell us when relevance is actually getting better, not just when a model is shipping
- Bring clarity. Translate ambiguous, cross-team problems into clear product plans, sequenced investments, and decisions the executive team can act on
Benefits
- Medical/Dental/Vision/FSA
- Wellness Programs & Social Events
- Life/Disability Insurance
- Matching 401k
- Employee Referral Bonus Program
- Discounted Gym Membership
- Tax-Free Commuter Benefits
- 5 to 8 years of product management experience, including at least 3 to 4 years in ad tech
- Comfort operating with autonomy in fast-moving, ambiguous environments, with the grit to drive decisions when data is incomplete
- Strong product instincts for search and relevance: retrieval, ranking, query understanding, evaluation, and the experimentation methods that actually surface causal lift
- High technical literacy with hands-on prototyping and technical scoping experience. Comfort reading SQL, exploring data, sketching pipelines, and working through model evaluation plans with ML and data science partners
- Excellent written communication. You can take a tangled, cross-team problem and make it legible to an executive in one page
- Working fluency in the publisher and SSP ecosystem, including supply-side workflows, integrations, feed quality, and marketplace dynamics
- Proven track record building data, ML, or AI products end to end. You have shipped something real that depended on data pipelines, feature engineering, or model output, and you can speak to the tradeoffs you made
- Background in data platform or feature store product work
- Experience working with or integrating against DSPs, SSPs, identity providers, or product feed / catalog systems
- Direct experience with AI Chat, generative search, or LLM-powered surfaces, especially monetization, ranking, or retrieval for those surfaces
- Hands-on experience with experimentation platforms, offline evaluation, and online A/B testing at scale