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Llm Annotation Jobs in California (NOW HIRING)

We are seeking candidates with strong linguistic data analysis and language technology experience to manage data collection, LLM-powered data synthesis and data annotation tasks, prompt engineering ...

ML Engineer

Los Angeles, CA · On-site

$132K - $165K/yr

Partner with Data Science on annotation workflows, PII scrubbing, and ground-truth pipelines ... Practical experience with evaluation for ML or LLM systems - golden datasets, model-as-a-judge, IAA ...

Data Labeling Associate

San Diego, CA · On-site

$17 - $22/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Deliver detailed reports on findings, including aspects such as utterance quality, LLM evaluation ...

Data Labeling Associate

San Diego, CA

$17 - $22/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Deliver detailed reports on findings, including aspects such as utterance quality, LLM evaluation ...

Experience designing or deploying agentic workflows to improve engineering productivity, data analysis, evaluation efficiency, or annotation quality. Familiarity with LLM-based systems, retrieval ...

Drive LLM/VLM inference optimization, including batching, scheduling, quantization, model serving ... Lead technical strategy for automated dataset annotation, filtering, quality scoring, deduplication ...

Showing results 21-40

Llm Annotation information

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 may prefer prior experience in data labeling or related fields. Training is often provided by employers to ensure accurate annotation of large language model datasets.

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.

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 cities in California are hiring for Llm Annotation jobs?

Cities in California with the most Llm Annotation job openings:

Infographic showing various Llm Annotation job openings in California as of August 2026, with employment types broken down into 83% Full Time, 12% Part Time, 2% Temporary, and 3% Contract. Highlights an 76% Physical, 5% Hybrid, and 19% Remote job distribution.

Facebook Requirement - Linguist II

Prolim Global

Burlingame, CA • On-site

Contractor

Re-posted 20 days ago


Job description

PROLIM (www.prolim.com) is currently seeking Linguist II for one of our top Client for Location- Burlingame, Seattle,Newyork

Qualified candidates can directly send your updated resume and contact info via email: alec.dsouza@prolim.com
Job Summary:

Summary

We are looking for a Linguist to help us develop language components for a variety of voice-enabled technologies and products. We are seeking candidates with native or near-native fluency in Hindi, Telugu and/or Bengali with strong linguistic data analysis and language technology experience to manage data collection, LLM-powered data synthesis and data annotation tasks, prompt engineering, localization and quality evaluations.

Job Responsibilities

• Provide linguistic expertise in the areas of syntax, semantics, pragmatics and sociolinguistics

• Collaborate with other linguists and data operations teams in data collection, data curation, translation, localization and annotation efforts

• Evaluate and curate data sets for ML models using LLM solutions

• Assess model and data quality

• Prompt engineering

• Collaboratively develop complex and consistent linguistic analyses

Required Qualifications

• Master’s degree in general Linguistics or Linguistics with an emphasis on Romance languages, Computational Linguistics, Speech Science, or related field

• Native or near-native fluency in Hindi, Telugu and/or Bengali

• Awareness of Indian languages and their linguistic, cultural, local nuances

• Knowledge of syntax, semantics, pragmatics, sociolinguistics, corpus linguistics, and other areas of linguistics

• Experience working with speech and text data in multiple languages

• Familiar with Large Language Models (LLMs), prompt engineering and their applications

• Comfortable working in a fast paced, highly collaborative, dynamic work environment

• Strong organizational skills and detail oriented

• Excellent communication skills both verbal and written

• Experience with database queries and data analysis processes (i.e. SQL, spreadsheets, R, Unix, or others)

Preferred (additional) Qualifications

• PhD in Linguistics or Romance languages, language technologies, computational linguistics, speech science, or related field

• Proficiency in Python

• Experience with machine learning frameworks, NLP Libraries and Tools

About PROLIM

PROLIM is a leading provider of PLM, IoT and Digital transformation solutions to Global Fortune 1000 companies. With 9 global offices in US, India, and Australia, PROLIM has won 30+ awards and proudly serving over 1200+ customers to innovate and improve their profitability and efficiency. PROLIM was founded in 2005 and is headquartered in Farmington Hills, USA. With the global footprint and expertise in latest technologies, PROLIM can partner with you to speed up your Digital Transformation journey.