Background in sports analytics or applied computer vision
Enthusiasm for sports and AI
Responsibilities
Design and develop AI systems for real-time video understanding
Build robust computer vision pipelines for challenging footage
Explore novel techniques in computer vision for sports
Develop AI systems for large-scale game data analysis
Create frameworks for actionable insights from video data
Design analytics platforms for league-wide deployment
Translate video understanding into engaging fan experiences
Benefits
Opportunity to publish research in top-tier venues
Collaboration with Carnegie Mellon University researchers
Mentoring opportunities for students
Creative ownership from prototype to production
Engagement in impactful projects that reach millions of fans
Full Job Description
Description
YinzCam is seeking exceptional Research Engineers to lead the development of AI-driven video analysis and game analytics systems that power next-generation fan experiences in professional sports. This is a rare opportunity to conduct publishable research while building products that reach millions of fans in real time.
You'll work at the cutting edge of computer vision and machine learning applied to sports, collaborating with leading academic researchers at Carnegie Mellon University while taking your innovations from prototype to production. This role demands both research rigor and product sensibility. We value publication records and engineering excellence equally. This is a full-time, onsite position based in Pittsburgh, PA.
You will be at the forefront of establishing a new, in-house AI Research Lab within YinzCam, and working with multiple sports teams, leagues, and venues to apply AI to the fan experience and to business operations.
CORE RESPONSIBILITIES.
Video Analysis & Computer Vision
Design and develop AI systems for real-time video understanding of live sporting events (player detection, action recognition, spatial analysis, etc.)
Build robust computer vision pipelines that handle challenging real-world footage (lighting, occlusion, multiple camera angles)
Explore novel architectures and techniques in modern CV to solve sports-specific problems
Large-Scale Game Analytics
Develop AI systems to extract, aggregate, and interpret game data at scale across multiple sports, teams, and seasons
Create spatial and temporal analytics frameworks that surface actionable insights from video and sensor data
Build analytics platforms that scale from single games to league-wide deployments
AI-Powered Fan Experiences
Translate video understanding and analytics into engaging, intuitive experiences for millions of fans
Collaborate on product features that leverage AI (real-time highlights, personalized stats, interactive visualizations, etc.)
Ensure research outputs move through the full product development lifecycle
CORE GOALS.
Publish Your Work: We intend to publish the work coming out of these research projects. Papers will be published in top-tier CV/ML venues and presented at conferences.
Bridge Academia & Industry: Work directly with Prof. Priya Narasimhan (Carnegie Mellon University) and her research team to translate academic innovations into applied systems. Mentor CMU students, collaborate on research projects, and shape the next generation of sports AI researchers.
From Research to Product: Own the path from prototype to production. You'll participate in design reviews, handle real-world deployment challenges, and see your ideas impact actual fan experiences at scale.
CORE REQUIREMENTS.
PhD in Computer Vision, Machine Learning, Computer Science, or a closely related field
Strong publication track record in top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, etc.)
Deep expertise in modern computer vision techniques: neural networks, object detection, semantic/instance segmentation, action recognition, optical flow, pose estimation, or related areas
Proficiency in ML frameworks (PyTorch, TensorFlow) and modern deep learning practices
