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Llm Annotation Jobs in California (NOW HIRING)

BC Forward

Linguist II (LLM/AI)

Sunnyvale, CA · On-site

$36 - $40.20/hr

Develop and maintain annotation schemas and guidelines for LLM training data, including instruction-tuning, preference labeling, and RLHF. Evaluate and quality-check datasets used for pre-training ...

Turing

Client Director, Frontier Data - US

Palo Alto, CA · On-site

$255K - $325K/yr

... annotation accuracy checks, and pipeline consistency * Ensure datasets adhere to compliance standards (PII, GDPR, HIPAA) and can be programmatically tested for usability and quality LLM Training ...

Cerebras

Client Director, Frontier Data - US

Palo Alto, CA · On-site

$255 - $325/hr

... annotation accuracy checks, and pipeline consistency * Ensure datasets adhere to compliance standards (PII, GDPR, HIPAA) and can be programmatically tested for usability and quality LLM Training ...

Turing

Client Director, Frontier Data - US

Palo Alto, CA · On-site

$255K - $325K/yr

... annotation accuracy checks, and pipeline consistency * Ensure datasets adhere to compliance standards (PII, GDPR, HIPAA) and can be programmatically tested for usability and quality LLM Training ...

Apple

Data Scientist

Sunnyvale, CA

$184K - $324K/yr

  • Medical

  • Dental

  • Retirement

Identify opportunities to leverage agentic systems, LLM-based workflows, and AI-assisted tooling to improve efficiency and quality in evaluation, data analysis, annotation, and failure investigation.

Bedrock Robotics

Agentic Data Understanding

San Francisco, CA · On-site

$134K - $162K/yr

Experience building agentic or LLM-orchestrated pipelines (e.g., using VLMs for zero-shot or few-shot annotation). * Familiarity with robotics data formats and sensor modalities (LiDAR, cameras, IMU)

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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 are popular job titles related to Llm Annotation jobs in California?

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What job categories do people searching Llm Annotation jobs in California look for?

The top searched job categories for Llm Annotation jobs in California are:

What cities in California are hiring for Llm Annotation jobs?

Cities in California with the most Llm Annotation job openings:

Llm Annotation jobs near you
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.

Data Scientist - Survey Design, Data Annotation, and Machine Learning Evaluation

Apple

Cupertino, CA

$144K - $263K/yr

Full-time

Medical, Dental, Retirement

Re-posted 23 days ago

Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers

Job description

Apple is where individual imaginations gather together, committing to the values that lead to
great work. Every new product we build, service we create, or experience we deliver is the
result of us making each other’s ideas stronger. The diversity of our people and their thinking
inspires the innovation that runs through everything we do. When we bring everybody in, we
can do the best work of our lives. Here, you’ll do more than join something - you’ll add
something.
Description
The Special Projects team at Apple is developing novel user-facing conversational features that
leverage the multimodal capabilities of state-of-the-art foundation models. As part of this
process, we generate real-world and simulated data, gather human data annotations, analyze
the results, and use them to build and evaluate Large Language Model judges. We are looking
for a skilled Data Scientist to join our Machine Learning Evaluations teams. This person will
work closely with ML Engineers to manage and analyze our human and automated data
annotation processes, and to develop, test, and refine LLM judges for generative AI model
evaluation. A successful candidate is experienced in survey design, data annotation, LLM
prompt engineering and prompt optimization, and has strong statistical analysis skills.","responsibilities":"Work closely with ML Engineers to understand data annotation needs
Design and manage data annotation processes, including the development of user
instructions, annotation pipeline processing, and process improvement
Develop LLM auto-judges and judging criteria for generative AI model evaluation
Analyze collected data annotations to assess and refine LLM auto-judges
Preferred Qualifications
PhD in Data Science, Statistics, or a quantitative social science field
Hands-on industry experience with product-focused statistical analysis
Experience working with large-scale multimodal data and data-annotation pipelines
Experience with LLM prompt engineering & prompt optimization
Experience with LLM auto-judges for generative AI model evaluation
A track record of publications or technical presentations in Data Science or a related field
Excellent at cross-functional collaboration
Minimum Qualifications
BA or Master’s degree in Data Science, Statistics, or a quantitative social science field
2+ years of hands-on experience working in survey design and human data annotation
Proficiency in Python
Excellent communication skills
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $144,600 and $263,800, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

What Apple employees say

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Benefits

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Workplace

Get the full story on Breakroom

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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