About Gracenote

Gracenote, a Nielsen company, is dedicated to connecting audiences to the entertainment they love, powering a better media future for all people. As the content data business unit of Nielsen, Gracenote powers innovative entertainment experiences for the world's leading media companies. Our entertainment metadata and connected IDs deliver advanced content navigation and discovery to connect consumers to the content they love. Gracenote's industry-leading datasets cover TV programs, movies, sports, music, and podcasts in 80 countries and 35 languages. We provide common identifiers that are universally adopted by the world's leading media companies, enabling powerful cross-media entertainment experiences. Machine-driven, human-validated best-in-class data and images fuel new search and discovery experiences across every screen.

Role Overview

As a Principal MTS - Data Science, you will serve as the primary technical architect for our AI/ML ecosystem. You will be responsible for defining the long-term technical vision for media understanding and generation, moving beyond tactical project delivery to build the foundational frameworks that power our next generation of products. This role requires a unique blend of scientific leadership, technical excellence, and the ability to influence cross-functional teams and executive stakeholders without formal authority. You will de-risk new initiatives through prototyping, lead the design of multimodal data foundations, and ensure that our systems scale to meet the demands of a global media catalog.

Key Responsibilities

• Scientific and Strategic Leadership: Shape the technical vision for AI/ML across the content product ecosystem. Identify high-impact opportunities for Generative AI, agentic solutions, and computer vision to transform content promotion, distribution, and metadata extraction.

• System Architecture Design: Architect complex, multimodal machine learning systems that integrate visual, audio, and textual data. Design end-to-end ML data foundations for efficient, reliable data annotation, processing, and storage at petabyte scale.

• Technical Excellence and Innovation: Lead the design of horizontal foundational capability layers ("shared paved paths") that scale across use cases, replacing siloed builds with unified platforms. Evaluate and integrate emerging research in diffusion models, vision transformers, and multi-agent architectures.

• Inference Optimization and Scalability: Oversee the development of high-performance inference systems, utilizing GPU acceleration and optimization techniques (quantization, pruning, TensorRT) to achieve optimal accuracy-latency trade-offs for real-time and batch workloads.

• Evaluation and Observability: Define rigorous and scalable evaluation frameworks, leveraging A/B testing, offline/online evals, and human-in-the-loop reviews. Implement telemetry for algorithm and workflow observability to ensure the reliability and health of deployed systems.

Mentorship and Organizational Influence: Serve as a domain expert and thought leader, mentoring the data science and ML engineering communities. Partner with executive leadership to align engineering goals with the company's broader strategic vision.

• Core Technical Skills: Expert-level proficiency in Python, Java, or C++, with a solid understanding of multi-threading, memory management, and distributed computing (Spark, Flink).

• Deep Learning and AI: 10+ years of experience in machine learning, with at least 2 years in LLMs, diffusion models, or other generative image/video models. Proficiency in deep learning frameworks like PyTorch or TensorFlow.

• Computer Vision Expertise: Extensive experience in object detection (YOLO), image segmentation (Mask R-CNN), and video understanding. Familiarity with NVIDIA DeepStream, Triton Inference Server, and TensorRT.

• Big Data and MLOps: Proven track record of deploying large-scale ML systems in production. Experience with Kafka, Airflow, Kubernetes, and cloud AI services (AWS Bedrock, SageMaker).

• Communication: Exceptional written and oral communication skills, with a proven ability to translate complex technical concepts into business value for diverse audiences.

• Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, Data Science, or a related quantitative field.

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