The NBA is looking to solve the problem of enhancing call accuracy, streamlining game flow, and improving transparency in officiating decisions through the development of real-time, multi-modal officiating products.
Requirements
- Minimum of 5+ years of experience in building production ML data pipelines or ground truth labeling tools
- Proficiency in Python programming language
- Experience working with large-scale ML pipelines, dataset versioning, and training frameworks
- Strong understanding of low-latency, high-throughput system design and distributed computing
- Hands-on experience with sensor data such as cameras and lidar
- Familiarity with cloud platforms including AWS, GCP, or Azure
Responsibilities
- Define and implement the distributed (PB scale) ML data strategy for Automated Officiating systems
- Build, optimize, and maintain data pipelines for multi-modal sensor data, including video and tracking data
- Manage data labeling pipelines, establish ground truth taxonomies, and oversee version control
- Collaborate with modeling teams to integrate perception algorithms into officiating solutions
- Coordinate deployment of Automated Officiating outputs across various media outlets
- Develop profiling tools to identify and resolve system performance bottlenecks
Other
- Excellent problem-solving, communication, and interpersonal skills
- Opportunity to work on innovative sports technology projects with a global leader
- Remote work flexibility with options for onsite work in New York, NY, or Secaucus, NJ
- Collaborative and inclusive work environment fostering growth and learning
- Comprehensive health and wellness benefits
