Staff AI Linguist

LinkedIn • Mountain View, CA

6d • Hybrid

About The Position

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. Job Description 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.

Requirements

• BA/BS in Computational Linguistics, Linguistics, Language Technologies, or a related field
• 4+ years of industry experience owning end-to-end human judgment, operations, and quality workflows for AI development
• Proven experience leading medium-to-large evaluation or annotation programs in production environments
• Experience working cross-functionally with partners such as Engineering, Product, and Data Science to drive decisions and execution
• Experience developing evaluation frameworks for complex model or agent behaviors
• Experience building or improving scalable evaluation or annotation workflows
• Experience working with datasets and evaluation methods for LLMs or agentic systems
• Experience analyzing quality signals and using findings to improve guidelines, workflows, or model performance
• Experience with Python, or an equivalent language, for analysis, experimentation, metrics, or quality validation
• Ability to communicate clearly in writing and verbally, including documenting decisions and aligning across functions

Nice To Haves

• 5-7 years of overall industry experience
• MS or PhD in Computational Linguistics, Linguistics, Language Technologies or a related field
• Experience in more ambiguous, high-impact, or fast-evolving AI product areas
• Experience with LLM-as-a-judge, reward modeling, or model-based evaluation approaches
• Experience creating standards or frameworks used across multiple teams
• Experience influencing product or quality direction through evaluation insights
• Experience mentoring others in evaluation, annotation, or quality methods
• Experience supporting i18n evaluation or linguistic quality across markets

Responsibilities

• Lead cross-functional alignment with Engineering, Product, Data Science, domain SMEs, Trust/Legal, TPM, and vendor operations on evaluation strategy, quality goals, tradeoffs, and delivery across multiple initiatives
• Define and evolve evaluation frameworks for complex model and agent behaviors, including rubrics, rating scales, defect taxonomies, escalation criteria, and market-specific guidance for ambiguous, multi-step, and high-impact use cases
• Own end-to-end evaluation systems, including metrics, scorecards, regression sets, monitoring plans, scenario suites, and success criteria, and ensure outputs are repeatable, decision-useful, and adopted by partner teams
• Design and operationalize annotation and evaluation pipelines across internal and vendor platforms, including task design, QA gates, adjudication approaches, workflow maintenance, and documentation
• Drive development of human, synthetic, and adversarial datasets to improve evaluation coverage, identify blind spots, and support model iteration, LLM-as-a-judge systems, and reward model development
• Lead calibration strategy and disagreement analysis across human and model judgments; identify drift, root causes, and reliability issues, and translate findings into guideline updates, new edge cases, retraining opportunities, and product quality improvements
• Set and uphold quality standards for vendor and internal workforces, including onboarding, guideline training, audit design, escalation handling, and cost-quality tradeoff decisions across medium-to-large programs
• Lead error analysis and evaluation experiments; synthesize findings into clear recommendations and influence roadmap, launch readiness, and quality investments
• Define requirements for human judgment and evaluation tooling, and partner with Engineering on design, testing, rollout, and adoption
• Create reusable standards and best practices that scale across teams, and enable partners on methodology, score interpretation, and appropriate use of evaluation outputs
• Mentor junior team members on evaluation design, annotation quality, analysis methods, and operational excellence
• Demonstrate learning agility in a rapidly evolving field by incorporating new tools, methods, and research into evaluation strategy and workflows
• Apply native-speaker linguistic and cultural expertise in French (France), German (Germany), Spanish (Spain), Portuguese (Brazil), or other i18n market(s) to define market-appropriate quality standards and improve consistency across locales

Benefits

• We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels.
• LinkedIn is committed to fair and equitable compensation practices.
• The pay range for this role is $133,000 - 216,000.
• Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location.
• This may be different in other locations due to differences in the cost of labor.
• The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans.
• For more information, visit https://careers.linkedin.com/benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level