AI / ML Engineer II (P2)
Nielsen
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.
About the Role:
As a Machine Learning Engineer at this level, you will be a key contributor to our team, responsible for building and deploying end-to-end machine learning models. You will work with a degree of autonomy on well-defined projects, translating business needs into functional and scalable ML solutions. This role is perfect for hands-on ML Engineers looking to deepen their expertise and take on more complex challenges.
Responsibilities:
● Independently build, train, and deploy machine learning models for complex projects.
● Provision, configure, and manage core AWS infrastructure supporting ML training and deployment pipelines—utilizing VPC for networking, EC2 and EKS for compute and Kubernetes container orchestration, EMR for big data processing, S3 for artifact and data storage, and RDS for relational database management.
● Design and maintain robust, end-to-end ML pipelines, from data processing to model serving.
● Contribute to technical design discussions and provide input on system architecture and best practices.
● Collaborate with business, product, and other engineering teams to understand requirements and translate them into technical specifications.
● Write high-quality, production-ready code and participate in code reviews to maintain our standards of excellence.
● Mentor interns or junior engineers, sharing your knowledge and expertise.
Basic Qualifications:
● Bachelor's or Master's degree in Engineering, Mathematics, Statistics, or a related field.
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