Editorial Prompt Engineer - AI Linguist

About the position 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.

Responsibilities

• Drafting annotation guidelines and developing robust, scalable annotation plans.
• Analyzing annotation results to assess data quality, consistency, and model readiness (including inter-annotator agreement and reliability metrics).
• Drafting and testing generative AI prompts to power editorial and learning products at LinkedIn
• Evaluating the quality of outputs from prompts and classifiers developed by the Editorial AI team and cross-functional partners
• Workshopping prompt drafts and offering feedback to other members of the Editorial AI team
• Developing new ideas and solutions to solve generative AI challenges
• Keeping up to date on the latest trends and techniques in prompt engineering and generative AI
• Collaborating with cross-functional partners (engineers, product managers, designers) and other members of the LinkedIn editorial and LinkedIn Learning teams
• Working on several projects at once, occasionally on tight deadlines.
• Training other editors and partners in the basics of data annotation and prompt engineering.

Requirements

• 3+ years experience in linguistics, data annotation work (including guideline development or quality review), journalism, content strategy, or similar role
• Experience working with generative AI tools (e.g., ChatGPT, Claude, etc.)

Nice-to-haves

• Proficiency in Python (especially for prompt iteration, evaluation or data analysis).
• Demonstrated interest in AI/ML and/or natural language processing.
• Understanding of key prompt engineering/LLM concepts (few-shot prompting, model fine-tuning, etc.).
• Familiarity with SQL, Jupyter and/or Git.
• Experience working cross-functionally with technical teams.
• Outstanding verbal communication skills and strong analytical capabilities.
• Comfortable working in ambiguous environments and iterating quickly.
• Exemplary editorial judgment.
• Eagerness for experimentation and creative problem solving.
• Passion for learning new technologies and tools.
• Ability to see other people's points of view and welcome constructive feedback.

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