AI Engineer at Genius Sports

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.

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

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

Qualifications

3\+ years of experience building production ML, CV, 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 system capabilities into production

Strong modeling judgment across deep learning and classical ML, with experience choosing 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

Preferred Qualifications

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

Experience with ML/CV domains relevant to sports understanding, such as 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/or 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, especially soccer, American football, and basketball

The salary for this role is based on an annualized range of $170,000 - $200,000 USD. This role will also be eligible to take part in Genius Sports Group's benefits plan.

We enjoy an ‘office-first’ culture and maximize opportunities to collaborate, connect and learn together. Our hybrid working models differ depending on your role and location. Occasional travel may be required.

As well as a competitive salary and range of benefits, we’re committed to supporting employee wellbeing and helping you grow your skills, experience and career.

We strive to create an inclusive working environment, where everyone feels a sense of belonging and the ability to make a difference.

Let us know when you apply if you need any assistance during the recruiting process due to a disability.