Principal Product Manager, Artificial Intelligence & Computer Vision

PlayOn•Alpharetta, GA

1d•Hybrid

About The Position

PlayOn Sports is the leading platform for high school sports media, streaming live games to millions of fans and powering the next generation of prep sports experiences. We partner with schools, state associations, and athletic programs across the country to bring every play to every fan, wherever they are. PlayOn is investing heavily in its computer vision and artificial intelligence pipeline, which transforms raw game footage into highlights, text recaps and statistics. This work sits at the intersection of live video, machine learning, and high school sports fandom, and it has the potential to fundamentally change what it means to watch a prep sports game online. We're looking for a Principal Product Manager to take end-to-end ownership of a new product line focused on these capabilities, from strategy all the way to launch and including the operating rhythm that keeps it healthy at scale. This is a product leadership role focused on the most dynamic area in technology today. You'll set the vision, make the hard tradeoffs, and drive outcomes across engineering, data, design, and our external partners. This role is for you if you thrive in ambiguous, high-impact environments, have a track record of turning early-stage bets into shipped products, and are energized by the idea of making high school sports moments more discoverable and meaningful. This is an individual contributor role initially, with the expectation to grow a small team as the product line matures.

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 donâ€™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.

Nice To Haves

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.

Responsibilities

Set and own the multi-year vision for the product line, aligning business leadership, engineering, partners and vendors on scope, sequencing, and success metrics.
Translate business goals including fan engagement and growth, content quality and coverage, and school retention and growth, into a concrete product strategy spanning computer vision capabilities, data pipelines, and fan-facing surfaces.
Define and drive product line KPIs: accuracy benchmarks, processing latency, content coverage, and downstream product adoption.
Own the strategic relationship with our computer vision and AI vendors. Set the agenda, work to align roadmaps, and hold partners to their delivery commitments.
Evaluate new CV capabilities including object detection, OCR, region-of-interest framing, tracking and automated highlight segmentation, and make the call on what to integrate, when, and why.
Drive integrations forward with vendor engineering teams, unblocking hard problems and accelerating capability adoption.
Partner with Data Engineering to build a sport-agnostic game data model that works across sports, game formats, broadcast configurations and game data sources.
Influence cross-product roadmaps to ensure fan and school-facing surfaces keep pace with our AI pipeline and create critical feedback loops that inform modeling priorities.
Give Operations and Support teams the visibility and tooling they need to triage and resolve content delivery issues without engineering escalation.
Build the operating model for the product line, including sport-specific configurations, fallback handling and quality review workflows, and evolve it as the product scales.
Close the loop between output quality (human review, fan signals, partner feedback) and upstream model or configuration improvements.
Own health end-to-end. Surface risks early, resolve dependencies, and create transparency around content delivery, timing, quality and coverage.