Job Description
PlayOn Sports processes over 250,000 live high school sports games a year across NFHS Network, GoFan, and MaxPreps. We’re using AI and computer vision to turn those streams into real-time scores, player stats, automated highlights, and interactive fan experiences. As a Senior Engineer on the Streaming Intelligence team, you’ll build the human-in-the-loop and fan-in-the-loop interfaces that connect our computer vision pipeline to the people who use it — internal operators reviewing AI-generated stats and millions of fans engaging with AI-powered experiences across our three consumer brands.
In this role, you’ll own the interactive layer between AI and users: annotation review tools, real-time stat overlays, correction workflows, and fan-facing features that run at scale across web and mobile. You’ll work primarily in Python, ship features end-to-end, and think AI-forward — not just consuming model outputs, but designing interfaces and services that make AI systems better through human feedback and fan interaction.
The ideal candidate is a builder. You’re energized by shipping, comfortable with ambiguity, and excited about working at the intersection of AI and product. You don’t wait for a fully baked spec — you break down loosely defined problems and start delivering.
The outcomes you’ll deliver
• Production annotation review interface: Ship the first human-in-the-loop interface for the computer vision stats pipeline, enabling internal operators to review, correct, and approve AI-generated statistics in real time. Target: production-ready within six months.
• Fan-facing AI feature: Deliver at least one AI-powered fan experience — real-time stat overlays, interactive highlights, or personalized content — to one of our consumer brands (NFHS Network, GoFan, or MaxPreps). Target: live within nine months.
• Reusable AI development patterns: Establish the team’s standard UI component library and Python service templates for AI-forward development, enabling faster iteration on future human-in-the-loop and fan-in-the-loop features.
• Correction workflow at scale: Build feedback loops that capture human corrections and fan interactions and route them back into the AI pipeline, measurably improving model accuracy over time.
• Cross-brand consistency: Deliver interactive AI features that work reliably across NFHS Network, GoFan, and MaxPreps, adapting to each brand’s UX patterns while sharing a common service layer.
PlayOn Sports processes over 250,000 live high school sports games a year across NFHS Network, GoFan, and MaxPreps. We’re using AI and computer vision to turn those streams into real-time scores, player stats, automated highlights, and interactive fan experiences. As a Senior Engineer on the Streaming Intelligence team, you’ll build the human-in-the-loop and fan-in-the-loop interfaces that connect our computer vision pipeline to the people who use it — internal operators reviewing AI-generated stats and millions of fans engaging with AI-powered experiences across our three consumer brands.
In this role, you’ll own the interactive layer between AI and users: annotation review tools, real-time stat overlays, correction workflows, and fan-facing features that run at scale across web and mobile. You’ll work primarily in Python, ship features end-to-end, and think AI-forward — not just consuming model outputs, but designing interfaces and services that make AI systems better through human feedback and fan interaction.
The ideal candidate is a builder. You’re energized by shipping, comfortable with ambiguity, and excited about working at the intersection of AI and product. You don’t wait for a fully baked spec — you break down loosely defined problems and start delivering.
The outcomes you’ll deliver
• Production annotation review interface: Ship the first human-in-the-loop interface for the computer vision stats pipeline, enabling internal operators to review, correct, and approve AI-generated statistics in real time. Target: production-ready within six months.
• Fan-facing AI feature: Deliver at least one AI-powered fan experience — real-time stat overlays, interactive highlights, or personalized content — to one of our consumer brands (NFHS Network, GoFan, or MaxPreps). Target: live within nine months.
• Reusable AI development patterns: Establish the team’s standard UI component library and Python service templates for AI-forward development, enabling faster iteration on future human-in-the-loop and fan-in-the-loop features.
• Correction workflow at scale: Build feedback loops that capture human corrections and fan interactions and route them back into the AI pipeline, measurably improving model accuracy over time.
• Cross-brand consistency: Deliver interactive AI features that work reliably across NFHS Network, GoFan, and MaxPreps, adapting to each brand’s UX patterns while sharing a common service layer.
PlayOn is where high school sports come to life. Through GoFan, NFHS Network, and MaxPreps, we give every fan a front-row seat to the moments that matter most: the buzzer-beaters, the comeback wins, the senior nights, the rivalries that define a town.
We built our technology for the people who live and breathe high school athletics — the parents who never miss a game, the alumni still cheering from across the country, the communities that show up week after week. From buying tickets to watching a live stream to reliving the highlights, we make it simple to stay close to the sports and the athletes you love most.
Backed by KKR, we build the technology that powers high school athletics from the inside out: Schools trust us to handle ticketing, streaming, fundraising, concessions, merchandise, and more so the people running programs can stay focused on the athletes and fans we all serve together.
We're a growth-stage company on a mission to make high school sports more accessible, more memorable, and more connected than ever before.
When being there means everything, we make sure you never miss a moment.
Why You'll Love Working at PlayOn