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.
In this role, you can expect to
•Build and ship human-in-the-loop interfaces that enable internal operators to review, correct, and approve AI-generated sports statistics in real time across thousands of concurrent live streams.
• Design and develop fan-in-the-loop experiences — interactive highlights, live stat overlays, and engagement features — across NFHS Network, GoFan, and MaxPreps.
• Own features end-to-end: from data modeling and API design through frontend implementation and production deployment. You’ll ship, not just spec.
• Integrate AI/ML model outputs (computer vision, LLMs) into production applications, building the service layer between models and users.
• Develop reusable Python service templates and UI component patterns that establish the team’s standard for AI-forward development.
• Collaborate with computer vision engineers, product managers, and data teams to translate pipeline outputs into intuitive, performant user experiences.
• Contribute to system design and architecture decisions within the streaming intelligence program. Participate in code reviews, design reviews, and technical documentation.
• Help evaluate and integrate third-party tools and vendor APIs (annotation platforms, model serving infrastructure) as the platform scales.
To thrive in this role, you have
• 3+ years of professional software engineering experience with strong Python skills and a track record of building production web applications and APIs.
• Builder mentality — you’ve shipped end-to-end features from concept to production, not just maintained existing systems. You bias toward action and iterate quickly.
• Experience integrating AI/ML models into user-facing applications (LLM APIs, computer vision pipelines, or similar). You understand the practical challenges of making model outputs useful to real people.
• Solid fundamentals in API design, data modeling, and service architecture. You write clean, testable code and care about the systems you leave behind.
• Familiarity with cloud infrastructure (AWS EKS, S3) and modern data tooling (Snowflake, Kafka, or similar).
• Strong communicator who works well across disciplines — you can talk to a product manager about user flows and a data scientist about model outputs in the same afternoon.
• Bonus: frontend experience with React/TypeScript (especially interactive annotation or dashboard UIs), familiarity with sports data or video analytics, experience with annotation tooling (Roboflow, CVAT, Label Studio), or interest in developer experience and internal tooling.
How You Play
Ownership over Participation – You take responsibility for delivering outcomes against real milestones: annotation review in production within six months, a fan-facing AI feature live within nine.
Team over Stars – You partner across engineering, data, product, and external collaborators to make AI systems usable by the whole company and delightful for fans.
Growth over Comfort – You embrace rapid experimentation, AI-forward development, and the ambiguity of building new product categories where no established playbook exists.
Fairness over Popularity – You evaluate tools, patterns, and approaches objectively, choosing what’s right for the platform over what’s fashionable.
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