The Boston Red Sox are seeking to develop and deploy computer vision methods to transform raw video into actionable data at scale, driving improvements in player evaluation, training environments, and performance analysis.
Requirements
- Demonstrated experience developing and deploying computer vision models and algorithms using frameworks such as PyTorch, TensorFlow, Keras, OpenCV, or similar.
- Proficiency in Python or R, with experience handling large-scale video and image datasets.
- Experience applying computer vision methods to sports, biomechanics, or human movement data is a plus.
- Familiarity with database technologies (SQL) and data pipeline development.
- Background in machine learning model deployment, cloud computing environments, or scalable infrastructure.
- Experience leveraging AI-assisted analysis tools for identifying and implementing solutions.
Responsibilities
- Develop and refine computer vision models to extract meaningful features from video, generating new datasets that enhance player analysis and performance research.
- Collaborate with developers, analysts, and stakeholders to ensure computer vision outputs integrate effectively into organizational tools, systems, and workflows.
- Build pipelines for video processing, feature extraction, and event detection at scale.
- Partner with Baseball Sciences, Player Development, and other stakeholders to identify opportunities where video-derived data can enhance evaluation and training.
- Contribute to the broader innovation roadmap of the Baseball Sciences department identifying novel opportunities for computer vision and machine learning applications.
Other
- Bachelor’s, Master’s, or PhD in Computer Vision, Machine Learning, Computer Science, Engineering, Applied Mathematics, or a related quantitative field is preferred.
- Strong critical thinking skills, intellectual curiosity, and ability to communicate findings clearly to both technical and non-technical stakeholders.
- Knowledge of public baseball analytics research or prior experience working with sports performance data is a plus.
- Effective demonstration of universal competencies related to problem solving, teamwork, clarity of communication, and time management.
- Embodying our culture of honesty, humility, relentlessness, and commitment to DEIB.
