Role Snapshot
AI‑generatedSenior Software Engineer at PlayOn! Sports building AI-integrated interfaces for sports statistics and fan engagement across streaming platforms. Role focuses on shipping end-to-end features that connect computer vision/LLM outputs to production user experiences.
Job Description
•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.
• 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.
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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