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Llm Annotation Jobs in Seattle, WA (NOW HIRING)

Handshake

AI Red Teamer (LLM Generalist)

Seattle, WA · On-site

... data annotation and rubric-based scoring • Prior work in trust and safety, content moderation, QA, or security research • Subject matter expertise in any high-risk domain (cybersecurity ...

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Llm Annotation information

See Seattle, WA salary details

$12.5K

$47.2K

How much do llm annotation jobs pay per year?

As of Aug 22, 2026, the average yearly pay for llm annotation in Seattle, WA is $45,521.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,500.00 and $45,500.00 per year, depending on experience, location, and employer.

What is LLM annotation?

LLM annotation refers to the process of labeling or tagging data specifically for training and evaluating large language models (LLMs) like GPT or BERT. Annotators read text and apply labels, correct errors, or provide feedback to help improve the model's understanding and performance. This work is crucial for supervised learning, as well-annotated datasets help LLMs better recognize patterns, context, and meaning in human language. LLM annotation can involve tasks such as sentiment analysis, named entity recognition, or instruction following. Annotators often use specialized platforms or tools to complete their tasks efficiently and accurately.

What are the key skills and qualifications needed to thrive as an LLM annotation specialist?

To thrive as an LLM Annotation Specialist, you need strong analytical skills, attention to detail, and a background in linguistics, computer science, or a related field. Familiarity with annotation platforms, natural language processing (NLP) tools, and data labeling systems is typically required. Excellent communication, critical thinking, and the ability to follow guidelines precisely are valuable soft skills for this role. These skills ensure high-quality, accurate data annotation, which directly impacts the performance and reliability of large language models.

What are some common challenges faced by LLM annotation specialists, and how can they be addressed?

LLM Annotation specialists often encounter challenges such as interpreting ambiguous language data, maintaining annotation consistency across complex datasets, and keeping up with evolving guidelines. These can be addressed by participating in regular team syncs to clarify guidelines, using annotation tools with built-in quality checks, and collaborating closely with project leads and fellow annotators. Continuous learning and open communication help ensure high-quality, reliable data annotation and support professional growth within the AI and NLP fields.

What is the difference between Llm Annotation vs Data Labeler?

AspectLlm AnnotationData Labeler
Required CredentialsBasic computer skills, sometimes familiarity with AI toolsBasic skills, often on-the-job training
Work EnvironmentRemote or office-based, tech-focusedRemote or on-site, varied industries
Industry UsageAI, machine learning, NLP projectsVarious industries including marketing, healthcare, and tech
Search & Comparison IntentUnderstanding roles in AI data preparationGeneral data labeling tasks

In summary, Llm Annotation involves specialized annotation for large language models, often requiring familiarity with AI tools, while Data Labeler is a broader role focused on labeling data across multiple industries with minimal technical requirements.

How to become an Llm annotator?

To become an LLM annotator, candidates typically need strong language skills, attention to detail, and familiarity with data annotation tools. Many positions require a high school diploma or equivalent, and some companies provide training. Experience with machine learning or natural language processing can be beneficial but is not always necessary.

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Llm Annotation jobs near you

AI Red Teamer (LLM Generalist)

Handshake

Seattle, WA • On-site

Full-time

Re-posted 27 days ago

Job description

Job Summary:
Handshake is a company focused on creating a path to great careers for everyone, and they are seeking an AI Red Teamer to stress-test large language models. The role involves designing adversarial prompts to expose vulnerabilities in AI models, thereby supporting AI safety and model robustness for leading research labs.
Responsibilities:
• Craft creative prompts and multi-turn scenarios to stress-test AI guardrails across diverse risk categories
• Discover ways around safety filters, restrictions, and defenses using jailbreak, evasion, and prompt injection techniques
• Explore edge cases to provoke disallowed, harmful, or incorrect outputs
• Evaluate and score model responses against structured harm taxonomies and severity rubrics
• Document experiments clearly, including what you tried, why you tried it, and what it revealed
• Review and refine adversarial prompts generated by other team members
• Contribute to harm taxonomy development, calibration exercises, and inter-rater reliability work
• Collaborate with engineers, data scientists, and researchers to share findings and strengthen defenses
• Work with potentially disturbing content on a regular basis (see Content Warning below)
• Stay current on jailbreaks, attack methods, and evolving model behaviors
Qualifications:
Required:
• Strong hands-on experience using multiple LLMs (ChatGPT, Claude, Gemini, open-source models, etc.)
• Intuition for crafting adversarial prompts; familiarity with jailbreak or evasion techniques is a strong plus
• Creative, adversarial problem-solving skills
• Clear and thoughtful written communication
• Strong ethical judgment and the ability to separate adversarial thinking from personal values
• Self-directed, collaborative, and comfortable in feedback-heavy environments
• Curiosity, persistence, and comfort with frequent failure in experimentation
Preferred:
• Familiarity with Python or other scripting languages
• Experience working with LLM APIs or evaluation tooling
• Comfort with structured data annotation and rubric-based scoring
• Prior work in trust and safety, content moderation, QA, or security research
• Subject matter expertise in any high-risk domain (cybersecurity, chemistry, biology, medicine, law, finance, etc.)
Company:
Handshake is a college career network that helps students and recent graduates find their next opportunity. Founded in 2014, the company is headquartered in San Francisco, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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