Lead Machine Learning Engineer- 8+ years (Individual Contributor)

Company Description At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future. Gracenote, a Nielsen company, is dedicated to connecting audiences to the entertainment they love, powering a better media future for all people. Gracenote is the content data business unit of Nielsen that 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 and discover new ones. Gracenote’s industry-leading datasets cover TV programs, movies, sports, music and podcasts in 80 countries and 35 languages. Gracenote provides 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

: We are hiring a highly motivated Lead Machine Learning Engineer to build and scale production ML systems across text and image modalities. This is a hands-on individual contributor role for someone who can independently design and ship robust inference backends, automate training and deployment workflows, and improve model performance across both traditional ML and modern deep learning systems. You will work to productionize models ranging from LLMs, transformers, embeddings, retrieval systems, and classical ML models (such as XGBoost). This role will balance focus between scaling inference backends and training/deployment automation. We are looking for someone who is comfortable operating with a high degree of autonomy, mentoring other engineers, and making strong technical decisions in a fast-moving environment.