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.
Key Responsibilities
- Partner cross-functionally with Engineering, Product, Data Science, domain SMEs, Trust/Legal, TPM, and vendor operations to align on quality goals, tradeoffs, and delivery plans
- Define and maintain evaluation frameworks, rubrics, rating scales, defect taxonomies, and agent-specific guideline addenda for ambiguous and multi-step agent behaviors across evolving product use cases and i18n markets
- Design and run scalable evaluation systems, including metrics, scorecards, regression sets, monitoring plans, scenario suites, evaluation strategies, and success criteria for agent quality assessment
- Build and operate high-quality annotation and evaluation pipelines, including task design, in-context evaluation surveys, evaluation strategies, QA gates, adjudication, and workflow maintenance across internal and vendor platforms
- Generate, annotate, and validate high-quality human, synthetic, and adversarial data, including reasoning-rich judgments where needed, to improve evaluation coverage, identify blind spots, and support LLM-as-a-judge and reward model development
- Lead calibration, drift detection, and disagreement analysis between human and model judgments, and translate findings into edge cases, retraining opportunities, and quality improvements
- Manage vendor and internal workforce quality, including onboarding, task coordination, guideline training, audits, escalations, and cost-quality tradeoff decisions to maintain high annotation standards
- Run error analyses and workflow experiments, document results, and drive iterative improvements based on evidence
- Define requirements for human judgment and evaluation tooling, and partner on build, test, deployment, and adoption
- Establish reusable best practices, enable partner teams on evaluation methodology, criteria interpretation, and judge score usage, and mentor junior team members
- Demonstrate learning agility and adaptability in a fast-evolving field by quickly absorbing new tools, methodologies, and domain knowledge, staying current with changes, and continuously applying new learnings to improve evaluation quality 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 and uphold market-appropriate quality standards for AI products with i18n
Qualifications
Required Qualifications
- BA/BS in Computational Linguistics, Linguistics, Language Technologies, or related field
- 2+ years of industry experience owning end-to-end human judgment and quality workflows for AI development
- Proven ownership of medium-to-large evaluation or annotation initiatives (method + delivery)
- Demonstrated cross-functional collaboration with Engineering/Product/Data partners, including managing tradeoffs, dependencies, and execution risks
- Experience building datasets and evaluation workflows for LLMs and agentic systems, including prompt-based labeling and evaluation, hybrid human-in-the-loop review, automated validation or consistency checks, and iterative dataset development to improve model and agent performance
- Experience using AI-assisted data workflows to improve annotation, evaluation, or dataset quality and efficiency
- Experience with Python, or an equivalent language, for analysis, experimentation, metric development, sampling, and annotation or evaluation quality validation
- Ability to communicate clearly in writing and verbally, including documenting decisions and working effectively across functions
Preferred Qualifications
- 3-5 years of overall industry experience
- MS/PhD in a relevant field
- Experience supporting i18n evaluation/annotation consistency
- 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 $106,000 - $144,000. 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.
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.
