Job Description:
• Take end-to-end ownership of a new product line focused on AI and computer vision capabilities.
• Set the vision, make hard tradeoffs, and drive outcomes across engineering, data, design, and external partners.
• Define and drive product line KPIs: accuracy benchmarks, processing latency, content coverage, and downstream product adoption.
• Partner with Data Engineering to build a sport-agnostic game data model.
• Evaluate new CV capabilities including object detection and automated highlight segmentation.
• Build the operating model for the product line as it scales.
• Own health end-to-end, surfacing risks early, resolving dependencies, and creating transparency.
Requirements:
• 8+ years in product management, technical product management, or a leadership role driving complex cross-team initiatives end to end.
• A track record of leading an important company initiative - one where you were the person accountable for the outcome, working across multiple engineering teams, external partners, and business stakeholders.
• Working fluency with AI/ML concepts. You understand what modern ML systems can and can't do, you can reason about model quality tradeoffs, and you can hold your own in technical discussions with ML engineers and vendors. You don’t need to have led an AI product before; you do need to be ready to lead one now.
• Strong data fluency. Comfortable reading and writing SQL, interpreting quality metrics, and reasoning about schema design and pipeline tradeoffs.
• Experience owning external partner relationships at a strategic level, shaping integration contracts, holding partners accountable, and escalating effectively.
• Excellent communication. Crisp product writing, clear executive presence, and the ability to build alignment across technical and non-technical audiences.
• High tolerance for ambiguity; a proven instinct for creating structure and momentum in early-stage problem spaces.
• Preferred
• Experience with live video streaming infrastructure or media processing pipelines (encoding, segmentation, metadata extraction).
• Familiarity with computer vision capabilities relevant to sports (object tracking, OCR, scene segmentation, highlight detection).
• Background in sports media, sports data, or adjacent domains (prep sports, broadcast, sports tech).
• Exposure to modern data stacks (Snowflake, dbt, Hightouch) and event-driven data architectures.
• Experience working with external sports data platforms (MaxPreps, NCAA data feeds, or similar) as data sources or integration targets.
Benefits:
• Multiple medical insurance plans to choose from
• Dental, vision life and disability insurance
• Employee Emergency Fund
• Company equity (stock options)
• Open PTO policy
• 401K plan with company match
• Hybrid/flexible work environment
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