WORK OPTION:The NBA currently provides eligible employees the option of working remotely one day per week. Group Summary: The Basketball Strategy and Growth department is responsible for data collection, analysis and technology pertaining to all on:court activities. The group, in partnership with Referee Operations, oversees the Game Review Program to help drive improvements in referee performance and rules clarification initiatives. Basketball Strategy and Growth also leads pivotal initiatives focused on innovating and improving the NBA game, such as rules changes, improvements to the competition format and implementation of technologies to improve player health, game integrity and fan engagement. The Automated Officiating team is a new function within the Basketball Strategy and Growth department. This team is focused on innovating the on:court product through internally developed and deployed technologies. They spearhead key officiating technology initiatives from concept to launch, leveraging their cross:discipline expertise in real:time perception and sensing, computer vision, machine learning, and data analytics. The primary near:term focus of this team is deploying a system that can automatically detect and determine objective calls (e.g., out:of:bounds) in real:time during live NBA games. Position Summary: The NBA is seeking an experienced machine learning engineer to build machine learning infrastructure from the ground up and be a key contributor to the Automated Officiating team. This team sits within Basketball Strategy and Growth, and its primary goal is to develop advanced multi:modal automated officiating : leveraging computer vision and other sensing technologies : to enhance call accuracy, streamline game flow, and provide decision:making consistency and transparency. This is a small team that works like a startup within the NBA and provides significant opportunities for ownership and accelerated learning and growth. Ideal candidates will bring considerable expertise in building large:scale computer vision training infrastructure and owning ML pipelines in production. This role will report to the Engineering Lead and play a critical role in taking our product from 0 to 1, leveraging expertise typically found in autonomous vehicles, robotics, AR/VR, or other real:time ML:driven systems. Major Responsibilities :Design, deploy, and maintain large, distributed ML training and inference clusters. :Build and maintain scalable data pipelines that handle petabyte scale multi:modal sensor data, and own model training throughout the ML lifecycle. :Profile, debug and implement tooling as needed for comprehensive analysis and optimize system performance. :Have a strong sense of ownership and be excited to make technical contributions across the automated officiating system, e.g. sensor pipelines, ML data pipelines, training, modeling and evaluation pipelines etc. :Provide technical guidance and mentorship to other engineers on the team. :Be a guardian of the codebase and push for clean, well:tested and highly extensible code. :Stay up to date on research and actively contribute new ideas. :Research and test different training approaches including parallelization techniques and numerical precision trade:offs across different model scales. Qualifications: :Bachelor's degree in Computer Science, Electrical Engineering, Math or related field (or equivalent experience). :Strong grasp of latest techniques to optimize training and inference workloads. :Demonstrated proficiency with distributed training frameworks to train large models. :Knowledge of various cloud platforms (GCP, AWS, Azure) and their Machine Learning service offerings. :Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker). :Background working on distributed task management systems and scalable model serving and deployment architectures. :Understanding of monitoring, loggi