AI Engineer

Genius Sports Group is seeking an AI Engineer to join the Sports AI team in New York, NY with a hybrid work setup. The role offers a yearly salary range of USD 170,000 to 200,000 and focuses on building advanced AI systems for sports analysis, automation, and insights.

Role Summary

The AI Engineer will contribute to the development of next generation applied AI platforms within the Sports AI group, delivering systems that enhance sports analytics, automate processes, and generate actionable insights from live and historical data.

Responsibilities

• Own applied AI work end to end, from data exploration and early prototypes through evaluation, production integration, and iterative improvement.
• Develop models, algorithms, and inference pipelines that convert sports data into structured events, predictions, insights, and confidence-aware outputs.
• Build models for event detection, event likelihood estimation, fan interest and 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 metrics, evaluation datasets, and benchmarks to measure AI system quality and guide model, algorithm, and product decisions.
• Train, adapt, evaluate, and integrate ML models and AI components, including multi-step systems where model, algorithmic, and LLM/agent outputs are composed, validated, and refined.
• Design workflows that use human review or correction data to improve evaluation, model iteration, and production output quality where appropriate.
• Collaborate closely with CV engineers on training pipelines, labeling workflows, and model deployment patterns.
• Partner with product, data platform, infrastructure, and systems engineers to integrate evaluated AI outputs into real-time sports products and automation workflows.
• Mentor junior teammates and contribute to team knowledge-sharing, reviews, and experiment design.

Requirements

• 3+ years of experience building production ML, computer vision, or AI systems.
• Ability to translate ambiguous sports product goals into concrete ML tasks, including defining the prediction target, identifying the right data, measuring output quality, and shipping production-ready solutions.
• Hands-on production ML/AI experience, including constructing datasets, defining features and labels, training and deploying models, evaluating outputs empirically, and shipping AI capabilities into production.
• Strong modeling judgment across deep learning and classical ML, with experience selecting approaches based on data inputs and problem structure.
• Experience with predictive modeling, event detection, data labeling, data quality improvement, and communicating experiment results to technical and non-technical stakeholders.
• Ability to evaluate AI system quality beyond anecdotal inspection, including reasoning about ambiguous outputs, imperfect labels, uncertainty, and real-world product tradeoffs.
• Strong production engineering fundamentals, including testing, observability, performance, and reliability.
• Demonstrated interest in the fast-moving landscape of LLMs, latest models, agentic AI systems, and development frameworks.
• Comfortable working in fast-moving, iterative environments with evolving requirements.

Technologies

• AWS Bedrock
• Union
• Rust
• LLM APIs

Benefits

• Benefits plan

About the Role

Genius Sports is enabling a new era of sports by combining cutting edge technology with the most comprehensive live data available. This approach aims to deliver more immersive, interactive, and personalized experiences for fans worldwide. The AI Engineer, Sports AI role sits at the center of this effort, focusing on building applied AI systems that power sports analysis, automation, and insights.

We are seeking an AI Engineer on the Sports AI team to help construct the next generation of applied AI systems that drive sports analysis, automation, and insights. These systems leverage live and historical sports data to support product capabilities and real-time decision making.

Preferred Qualifications

• Hands-on experience with LLM integrated workflows, LLM APIs, or cloud AI platforms such as AWS Bedrock, agentic AI systems, multi-agent setups, or evaluating LLM/agent outputs in production workflows.
• Experience with ML or CV domains relevant to sports understanding, including action recognition, sequence modeling, multimodal modeling, object detection, tracking, or player identification.
• Experience working with player tracking data, sports analytics, play-by-play data, labeling platforms, and ML training platforms such as Union.
• Experience collaborating with CV engineers or integrating CV model outputs into downstream ML workflows.
• Experience using human review or correction workflows to evaluate and improve AI system quality.
• Experience building production systems in Rust.
• Familiarity with streaming, event-driven, audio/video, or real-time data workflows is a plus.
• Background or strong interest in sports, particularly soccer, American football, and basketball.