Full-time
Hybrid
IC3/8
Product
LinkedIn is the world's largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We're also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that's built on trust, care, inclusion, and fun - where everyone can succeed. Join us to transform the way the world works.
This role will be based in New York, Mountain View or San Francisco. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.
LinkedIn was built to help professionals achieve more in their careers, and every day millions of people use our products to make connections, discover opportunities and gain insights. Our global reach means we get to make a direct impact on the world's workforce in ways no other company can.
LinkedIn is seeking a thoughtful, creative prompt engineer and AI linguist with strong editorial judgment to join our Editorial AI team. This team sits at the intersection of LinkedIn News and LinkedIn Learning, exploring how generative AI can improve editorial workflows, develop innovative member-facing products and power the future of learning on LinkedIn.
The ideal candidate will possess experience in AI data annotation design, annotator training, content strategy, or linguistics, along with a strong interest or experience working in machine learning and/or data analysis. They will be adept at working cross-functionally; experience working with product managers and engineers is a plus.
This role focuses on designing and evaluating annotations involving editorial and natural language classification tasks. It will involve validating large-scale editorial data annotations used to develop, evaluate, and vet machine-learning classifiers and generative AI systems. This role requires creative thinking, collaborative problem solving and a keen interest in exploring how to build new tools and solve different editorial challenges using generative AI. Coding experience, particularly with Python, is a plus. You will also be a nimble operator, able to juggle multiple projects and deadlines at once. The ideal candidate will be cool-headed under pressure and comfortable working on projects that may involve ambiguity and no single, immediate solution.
Data annotation design and guidelines development