CV / ML Engineer, Automated Officiating

National Basketball Association (NBA)•New York, NY

1d•Hybrid

About The Position

The Automated Officiating team is a new function within the Basketball Strategy & 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. The Automated Officiating team at the NBA is seeking an experienced engineer with a strong foundation in Computer Vision and Machine Learning. This is a senior position, and candidates ideally have Technical Leadership experience related to the development and deployment of advanced computer vision capabilities applied to highly ambiguous problems. The team sits within Basketball Strategy & Growth, and its primary goal is to develop advanced, multi-modal officiating capabilities 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 bring considerable expertise in applying state-of-the-art computer vision techniques from other fields to reason about scene and player level semantics, player actions and intent, player and ball tracking, and 3D reconstruction and mesh tracking of dynamic objects, with the ultimate goal of building a high-accuracy system that is able to make live calls for objective violations using cameras and other sensing modalities. We are looking for candidates who have the skills and aptitude to work on highly complex and ambiguous problems and are excited to contribute to all aspects of a real-world perception system, from building sensing pipelines to scalable ML data, training, modeling and evaluation pipelines.

Requirements

Masters, or Ph.D. in Computer Science, Computer Engineering, Math or related field (or equivalent professional experience).
Experience leading projects and driving execution of complex and ambiguous initiatives.
Experience working with ML data pipelines and large datasets (TB or PB scale) in a production environment.
Proven track record of breaking complex and ambiguous problems into understandable chunks, and mapping to applicable modern solutions.
Familiarity with containerization and orchestration frameworks like Kubernetes, Docker.
Proficiency with at least one deep learning framework (Pytorch, TensorFlow, JAX etc).
Exposure to the entire ML stack, from data pipelines to model inference.
Excellent problem-solving skills and adaptability in a fast-paced environment.
Excellent communication and interpersonal skills.

Nice To Haves

Proven experience delivering solutions for real-world perception challenges (e.g., AR/VR, autonomous vehicles, robotics, drones).
Strong C++ programming skills (or another equivalent compiled on-board language), with a history of optimizing and deploying performance-critical systems.
Familiar with ML training frameworks and prior experience building ML training and evaluation pipelines.
Experience with production ML systems, including scalable data pipelines, training infrastructure, model evaluation or deployment.
Familiarity with computer vision libraries, model deployment (TensorRT, ONNX) and GPU acceleration frameworks.
Strong grasp of low-latency, high-throughput system design, distributed task management systems and scalable model serving & deployment architectures.
Exposure to CUDA, parallel computing, or high-performance programming on GPUs.
Passion for basketball and familiarity with officiating rules.

Responsibilities

Designing, implementing, and deploying state-of-the-art tracking, 3D reconstruction and geometry estimation, scene understanding, and visual recognition systems.
Playing a role in defining the technical strategy, actively looking for problem areas, and proactively proposing solutions.
Make technical contributions across the automated officiating system, e.g. sensor pipelines, ML data pipelines, training, model development and evaluation pipelines etc.
Be a leader and an advocate for good ML design principles and software development practices.
Build and maintain efficient, scalable end-to-end pipelines to manage petabyte-scale multi-modal datasets and model training throughout the entire ML lifecycle.
Profile, debug and implement tooling to understand bottlenecks and optimize system performance.
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.