Machine Learning Engineer – Generalist

Machine Learning Software Engineer

WORK OPTION: The NBA currently provides eligible employees the option of working remotely one day per week.

Position Description:

The NBA is seeking an experienced machine learning software engineer to be a key contributor to the Automated Officiating team. This 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 will bring considerable expertise building and owning modern vision models and ML pipelines in production and are able to contribute to all aspects of a real-world perception system, from sensor processing pipelines to scalable ML data, training, modeling and evaluation pipelines. 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.

Group Summary:

The Basketball Strategy & 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 & 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 & 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.

Major Responsibilities:

• Make technical contributions across the automated officiating system, e.g. sensor pipelines, ML data pipelines, training, model development and evaluation pipelines etc.

• Designing, implementing, and deploying state-of-the-art tracking, 3D reconstruction and geometry estimation, scene understanding and visual recognition systems.

• 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.

• Collaborate with the broader Basketball R&D team on various initiatives, such as sensing research and development, KPI development and measurement, product road mapping, etc.

• Provide technical guidance and mentorship to other engineers on the team.

• Have a strong sense of ownership and be excited to wear many hats.

• Be a guardian of the codebase and push for clean, well-tested and highly extensible code.

Qualifications:

• Bachelor’s degree in Computer Science, Electrical Engineering, Math or related field (or equivalent experience).

• Experience working with ML data pipelines and large datasets (TB or PB scale) in a production environment.

• Demonstrated proficiency building and deploying machine learning solutions to production.

• Familiarity with containerization and orchestration frameworks like Kubernetes, Docker.

• Proficiency in Python and prior experience building machine learning data pipelines.

• 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.

Bonus Qualifications:

• 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.

Salary Range: $210,000 - $300,000