AI Linguist at LinkedIn3

This role will be based in Mountain View, CA. 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. HALO (Human Judgment, Annotation, Localization, and Operations) is a horizontal team within Core AI that partners across the company to enable high-quality human judgment for AI development. We partner closely with cross-functional stakeholders and internal teams to define quality goals, design evaluation and data pipelines, and scale repeatable measurement systems. Our work spans multiple initiatives at once, supported by shared standards, platforms, and best practices that help teams move faster without compromising quality. Key Responsibilities • Be part of working groups with Engineering, Product, Vendor Operations, etc. to align on quality goals, tradeoffs, and deliverables for assigned projects • Create annotation tasks end-to-end (rubrics, rating scales, decision rules, examples) and translate ambiguous behaviors into measurable criteria • Perform hands-on annotation and task completion for complex or high-impact cases to establish ground truth and calibrate guidelines • Build and operate scalable annotation and evaluation workflows on vendor or in-house platforms (task design, routing, sampling, quality assurance gates) • Support vendor and internal workforce execution through clear instructions, training materials, calibration support, and ongoing quality loops • Implement quality controls such as audits, inter-annotator agreement tracking, adjudication processes, and drift checks • Use AI tools to improve workflow speed and consistency (LLM-assisted prompting, hybrid labeling and evaluation, automated checks) with reliability controls • Create and validate datasets and evaluations used for training and assessing models and agents; perform error analysis and drive iteration with partners • Run practical experiments to improve quality or efficiency (rubric, prompt, task design, sampling changes) and document outcomes • Contribute requirements and feedback for workflow and tooling improvements