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Llm Annotation Jobs in Texas (NOW HIRING)

ClifyX

Python GenAI Model Evaluator

Austin, TX · On-site

PyTorch, TensorFlow, HuggingFace, LangChain. • Evaluation/annotation tools: Scale AI, GroundTruth, Labelbox, Prodigy. • Prompt testing tools: Weights & Biases, MLflow, OpenAI evals, LLM-as-a ...

Sourceability

Principal NLP Scientist

Austin, TX

Review existing models, datasets, annotation processes, and production use cases * Identify the highest-impact opportunities for NLP and LLM improvements * Define practical evaluation metrics for ...

HumanSignal

Delivery Lead

Austin, TX · Remote

$110K - $140K/yr

... LLM assistance Preferred Qualifications * 1+ year in AI data operations (RLHF, annotation, model evaluation) * STEM background or strong technical fluency * Python & REACT working knowledge

HumanSignal

Delivery Lead

Dallas, TX · Remote

$110K - $140K/yr

... LLM assistance Preferred Qualifications * 1+ year in AI data operations (RLHF, annotation, model evaluation) * STEM background or strong technical fluency * Python & REACT working knowledge

Aurigo Software Technologies Inc.

Agent Designer

Austin, TX · On-site

$14.75 - $19.75/hr

Hands-on experience with LLM-based tools, prompt engineering, or agentic workflow platforms * AI ... Familiarity with data labeling, annotation workflows, or training data quality processes

Marsh McLennan

Applied AI Product Manager

Austin, TX · On-site

Partner with engineering and AI teams on LLM-powered systems, including prompts, context design ... Experience with observability, quality dashboards, annotation workflows, or human-in-the-loop ...

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Llm Annotation information

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.

What are popular job titles related to Llm Annotation jobs in Texas?

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

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

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Cities in Texas with the most Llm Annotation job openings:

Llm Annotation jobs near you
Infographic showing various Llm Annotation job openings in Texas as of September 2026, with employment types broken down into 90% Full Time, 1% Part Time, 2% Temporary, and 7% Contract. Highlights an 75% Physical, 5% Hybrid, and 20% Remote job distribution.

Python GenAI Model Evaluator

Austin, TX • On-site

ClifyX
Recruiting and Staffing Services • 51 - 200 employees
Learn more about ClifyXexternal

Full-time

Re-posted 24 days ago

Job description

Job Summary:
ClifyX is a company specializing in AI-driven solutions, and they are seeking a Python GenAI Model Evaluator. The role involves evaluating generative AI models and requires a strong understanding of LLMs and related technologies.
Qualifications:
Required:
• Strong understanding of LLMs, generative AI, and transformer-based architectures.
• Experience with Python, data analysis, and model evaluation frameworks.
• Familiarity with prompt engineering, embeddings, RLHF/RLAIF, and LLM-based scoring methods.
• Experience building evaluation datasets and working with annotation platforms.
• Understanding of safety alignment, bias detection, and adversarial testing.
• ML/AI frameworks: PyTorch, TensorFlow, HuggingFace, LangChain.
• Evaluation/annotation tools: Scale AI, GroundTruth, Labelbox, Prodigy.
• Prompt testing tools: Weights & Biases, MLflow, OpenAI evals, LLM-as-a-judge pipelines.
Company:
ClifyX provides innovative business solutions which satisfy requirements for mission-critical reliability, scalability, interoperations. Founded in 1998, the company is headquartered in South Plainfield, USA, with a team of 501-1000 employees. The company is currently Late Stage.
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About ClifyX

Sourced by ZipRecruiter

ClifyX is a well-established player in the IT Services sector that specializes in providing result-oriented technological solutions to a wide range of industrial verticals. Based in South Plainfield, New Jersey, ClifyX offers a comprehensive selection of IT services that include project staffing, application development, professional consulting, and other IT-based solutions. While the company's website, clifyx.com, does not divulge the exact founding date, it is clear that ClifyX has grown into a renowned name within their domain, thanks to their unwavering commitment to innovative practices. The company's mission statement revolves around harnessing the power of technology to assist their clientele in steering their respective businesses towards success.

Industry

Recruiting and staffing services

Company size

51 - 200 Employees

Headquarters location

South Plainfield, NJ, US

Year founded

1998

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