Company Description
LinkedIn is the worlds 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. Were also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture thats built on trust, care, inclusion, and fun where everyone can succeed.
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.
Role Summary
AI is evolving rapidly and high-performing teams win by defining quality clearly, building reliable ground truth, and scaling human judgment without slowing innovation. HALO makes that possible.
The AI Linguist plays a key role in shaping the quality of LinkedIn’s AI systems across a wide range of use cases, experiences, and product areas, including but not limited to relevance, ranking, rationale quality, and emerging multi-step and agentic capabilities. This role turns ambiguity into clear evaluation standards by designing annotation tasks and rubrics, producing high-quality ground truth through hands-on annotation, and building frameworks that make AI systems, models, and agents trainable, measurable, and continuously improvable.
The role also drives scalable annotation and evaluation pipelines, conducts audits of internal and vendor-produced work, and helps ensure strong inter-annotator agreement and consistently high quality. Working at the intersection of human expertise and modern AI tooling, the AI Linguist applies methods such as LLM-assisted prompting, hybrid labeling and evaluation, automated checks, regression testing, continuous monitoring to support evolving business and product needs.
The datasets, rubrics, and evaluation signals produced in this role become shared standards across LinkedIn, directly influencing how AI systems are trained, evaluated, and improved. This is a great opportunity for someone who thrives in ambiguity, builds frameworks that others depend on, and wants to shape how AI performs in the real world.
Key Responsibilities
- Partner cross-functionally with Engineering, Product, Data Science, domain SMEs, Trust/Legal, TPM, and vendor ops to align on quality goals, tradeoffs, and delivery plans
- Define measurable quality criteria for ambiguous behaviors (rubrics, rating scales, concepts, failure modes) and ensure consistency across markets
- Design and run repeatable evaluation systems (metrics, scorecards, regression sets, monitoring plans), including multi-step/agentic behavior evaluation using scenario suites and success criteria
- Build scalable, high-quality annotation/evaluation pipelines, including hands-on execution of annotation task, covering task design, sampling, QA gates, adjudication, maintenance) on vendor and/or in-house platforms
- Lead vendor and internal workforce execution at scale, owning training and onboarding, calibration sessions, periodic reviews/audits of internal Linguists’ and vendors’ annotation output to measure and improve inter-annotator agreement, quality escalations and adjudication, and ongoing cost/quality tradeoffs to consistently maintain high annotation quality
- Establish and enforce quality governance (agreement targets, drift/bias checks, defect taxonomy)
- Leverage AI tools to scale work (LLM-assisted prompting, hybrid labeling/evaluation, automated checks) while maintaining reliability controls
- Run method/workflow experiments; document results and drive decisions based on evidence
- Perform error analysis and drive iteration cycles with partners; translate findings into actionable changes
- Define platform/tool requirements for human judgment workflows; partner through build/test/deploy and adoption
- Publish reusable best practices and standards; mentor junior Linguists and conduct design/analysis reviews across initiatives
Qualifications
Basic Qualifications
- BA/BS in Computational Linguistics, Linguistics, Language Technologies, or related field
- 2+ years industry experience in human judgment/annotation/evaluation supporting AI development
- Proven ownership of medium-to-large evaluation or annotation initiatives (method + delivery)
- Demonstrated cross-functional collaboration (Engineering/Product/Data partners) and ability to manage tradeoffs and dependencies
- Experience designing large-scale pipelines with QA governance and maintenance plans, including vendor/platform-based workflows
- Experience converting ambiguity into measurable criteria and repeatable evaluation methods
- Experience in applying AI-assisted data workflows as well as creating datasets for LLMs and agentic systems, prompt-based labeling and evaluation, hybrid human-in-the-loop review, automated validation/consistency checks, and iterative dataset building to improve model and agent performance
- Proficiency in Python (or equivalent) for analysis/experimentation, building metrics, sampling, and to validate annotation/evaluation quality (analysis-focused; not production software engineering)
- Ability to communicate cross-functionally and document decisions
Preferred Qualifications
- MS/PhD in a relevant field
- Experience supporting multi-market evaluation/annotation consistency (partnering with localization/language experts)
- Experience evaluating multi-step/agentic behavior with scenario suites, failure mode taxonomies, and continuous evaluation loops
- Experience scaling standards and frameworks across multiple teams
- Experience building semi-automated evaluation components (scorecards, monitoring, regression suites)
- Ability to execute across multiple concurrent initiatives
Suggested Skills
- AI evaluation and annotation frameworks
- Agentic AI Quality governance and human-judgment operations
- Cross-functional AI program execution
You will Benefit from our Culture
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 $141,000 - $252,500. 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.
Additional Information
Equal Opportunity Statement
We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.
LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful.
If you need a reasonable accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us at accommodations@linkedin.com and describe the specific accommodation requested for a disability-related limitation.
Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to:
- Documents in alternate formats or read aloud to you
- Having interviews in an accessible location
- Being accompanied by a service dog
- Having a sign language interpreter present for the interview
A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response.
LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information.
San Francisco Fair Chance Ordinance
Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.
Pay Transparency Policy Statement
As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.
Global Data Privacy Notice for Job Candidates
Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.
