AI Engineer

By bringing together next-gen technology and the finest live data available, Genius Sports is enabling a new era of sports for fans worldwide, delivering experiences that are more immersive, interactive and personalized than ever before. Learn more at geniussports.com.

About the Role - AI Engineer, Sports AI 

We're looking for an AI Engineer on our Sports AI team to help build the next generation of applied AI systems powering sports analysis, automation, and insights. 

These systems use live and historical sports data, including tracking data, structured feeds, broadcast video, commentary, and text, to understand game context, detect & enrich key events, estimate the probability of future events, and generate insights. The outputs from these systems power a range of products and workflows, such as projecting which games or moments will be most exciting to fans and automating parts of manual play-by-play collection using CV/AI. The role spans a broad set of sports modeling and automation problems across multiple sports, including soccer, American football, and basketball. 

This role sits at the intersection of machine learning, AI system design, and production engineering. You'll own scoped AI systems end-to-end: framing the relevant modeling problems, constructing the datasets needed to solve them, training and composing models and algorithms, building the inference pipelines that orchestrate them, and rigorously evaluating output quality against messy, real-world data. 

You'll work on challenges like aligning signals across multiple sources, handling uncertainty and inconsistency in system outputs, and improving accuracy, latency, and reliability in real-time production workflows. In this role, hands-on ML/AI work will be central: understanding data, developing models and algorithms, evaluating outputs empirically, and iterating in production. You'll also apply LLMs and agentic workflows as part of your broader AI engineering toolkit. 

 

Key Responsibilities  

• Own applied AI work end-to-end, from data exploration and early prototypes through evaluation, production integration, and iteration 

• Develop and compose models, algorithms, and inference pipelines that convert sports data into structured events, predictions, insights, and confidence-aware outputs 

• Build models for problems such as event detection, event likelihood estimation, fan interest & excitement projection, and automation of manual play-by-play collection 

• Work with messy, multimodal sports data from tracking systems, video and computer vision outputs, audio, commentary, text, and structured feeds, including imperfect labels and ambiguous real-world examples 

• Define and use metrics, evaluation datasets, and benchmarks to measure AI system quality and guide model, algorithm, and product decisions 

• Share