AI Engineer
Genius Sports is seeking an AI Engineer to join its Sports AI team. The role focuses on building the next generation of applied AI systems that power sports analysis, automation, and insights. These systems leverage live and historical sports data, including tracking data, broadcast video, and text, to understand game context, detect key events, and generate predictive insights.
The position sits at the intersection of machine learning, AI system design, and production engineering. The engineer will own scoped AI systems end-to-end, from framing modeling problems and constructing datasets to training models and building inference pipelines. The role involves working on complex challenges such as signal alignment, handling system uncertainty, and ensuring accuracy and reliability in real-time production workflows.
- 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 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 and use 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.
- Work 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.
- 3+ years of experience building production ML, CV, or AI systems (required)
- 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 (required)
- Hands-on production ML/AI experience, including constructing datasets, defining features and labels, training and deploying models, evaluating outputs empirically, and shipping AI system capabilities into production (required)
- Strong modeling judgment across deep learning and classical ML, with experience choosing approaches based on data inputs and problem structure (required)
- Experience with predictive modeling, event detection, data labeling, data quality improvement, and communicating experiment results to technical and non-technical stakeholders (required)
- Ability to evaluate AI system quality beyond anecdotal inspection, including reasoning about ambiguous outputs, imperfect labels, uncertainty, and real-world product tradeoffs (required)
- Strong production engineering fundamentals, including testing, observability, performance, and reliability (required)
- Demonstrated interest in the fast-moving landscape of LLMs, latest models, agentic AI systems, and development frameworks (required)
- Comfortable working in fast-moving, iterative environments with evolving requirements (required)
- Hands-on experience with LLM-integrated workflows, LLM APIs or cloud AI platforms such as AWS Bedrock, agentic AI systems, multi-agent systems, or evaluation of LLM/agent outputs in production workflows (preferred)
- Experience with ML/CV domains relevant to sports understanding, such as action recognition, sequence modeling, multimodal modeling, object detection, tracking, or player identification (preferred)
- Experience working with player tracking data, sports analytics, play-by-play data, labeling platforms, and/or ML training platforms such as Union (preferred)
- Experience collaborating with CV engineers or integrating CV model outputs into downstream ML workflows (preferred)
- Experience using human review or correction workflows to evaluate and improve AI system quality (preferred)
- Experience building production systems in Rust (preferred)
- Familiarity with streaming, event-driven, audio/video, or real-time data workflows (nice-to-have)
- Background or strong interest in sports, especially soccer, American football, and basketball (nice-to-have)
- Annualised salary range of $170,000 - $200,000 USD
- Participation in Genius Sports Group's benefits plan
- Support for employee wellbeing and career development
Genius Sports is a sports data and technology company that supplies official data, streaming, integrity and advertising services to sports leagues, sportsbooks and media companies. Founded in 2001 and headquartered in London, with a major office in New York and others worldwide, it is the official data partner of organisations including the NFL, English Premier League and FIBA. The company provides the technology that connects sports, betting and media businesses. Genius Sports is listed on the New York Stock Exchange.