Member Technical Staff II

Gracenote, the content data division of Nielsen, powers innovative entertainment experiences for leading media companies worldwide. We are seeking talented engineers to join our Gracenote Tech team. The ideal candidate cares about clean, well-designed code and can independently drive implementation from design through delivery. As an engineer on our In-stream Video Intelligence (ISVI) team, you will build the next-generation metadata platform that gives machines a rich, structured understanding of video content, combining strong engineering with state-of-the-art AI. You will create high-performance applications with a platform-first mindset, where scalability, consistency, and reliability sit at the core of every decision.

Responsibilities

Own problems end to end across the full stack, from frontend and backend to AWS infrastructure and the software–AI boundary; Design and operate scalable, reliable systems that process high volumes of video data with speed and accuracy; Integrate state-of-the-art foundational and open-weight models (LLMs, VLMs, vision AI) into production pipelines to extract structured understanding from video; Ship end to end, iteratively and fast, using AI coding assistants as part of your daily workflow; Build cloud-native, event-driven services on AWS with messaging and streaming systems (e.g., Kinesis, SQS) for high-throughput, asynchronous workloads

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related field; 3-5 years of professional software development experience; Strong proficiency in a modern programming language such as Python, Java, or Go; Strong computer science fundamentals: data structures, algorithms, concurrency, and distributed systems; Solid grasp of RESTful APIs, microservices architecture, and event-driven design; Experience across a range of data stores: relational, NoSQL, graph, and vector databases; Hands-on experience with cloud-native development (AWS preferred), containerization and orchestration (Docker, Kubernetes), and CI/CD practices; Fluency with an AI-native SDLC, using AI coding assistants and agentic tooling as a core part of how you design, build, and ship

Nice to have

Experience building and scaling systems at fast-paced startups and/or tech product companies, with real exposure to the complexities of scale; Applied AI exposure: vector embeddings, LLMs, VLMs, vision AI, and MLOps; Experience processing large-scale media (video/audio) or other high-volume data pipelines

Benefits

Competitive salary and benefits package; Opportunities for professional growth and development; A collaborative and inclusive work environment