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Llm Annotation Jobs (NOW HIRING)

Senior Software Engineer - LLM Trainer

$125.40K - $165.30K/yr

... annotation, or LLM evaluation projects • Excellent written and verbal communication skills in English • Ability to work independently in a remote, asynchronous, fast-paced environment • High ...

You will partner closely with leading LLM providers , enterprises, and internal teams to design solutions that span data labeling, annotation, localization, RLHF, and multi-modal workflows . This is ...

Data Scientist

Sunnyvale, CA

$181.10K - $318.40K/yr

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.

Experience with foundation models for data annotation * Experience with MLOps tooling (Weights & Biases, MLflow, SageMaker, or equivalents) * Experience shipping LLM- or agent-powered features in a ...

Experience with foundation models for data annotation * Experience with MLOps tooling (Weights & Biases, MLflow, SageMaker, or equivalents) * Experience shipping LLM- or agent-powered features in a ...

Strategic Projects Lead

$140K - $180K/yr

Research integration Stay current with developments in LLM post-training, evaluation methodology, and data tooling. Evaluate new approaches - model-assisted annotation, structured output formats ...

Support labeling initiatives, data annotation processes, and LLM training workflows to improve product quality and operational efficiency. * Drive operational rigor by managing multiple workstreams ...

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 ...

Roles & Responsibilities o Implement Guardrails and observability across RAG and LLM applications o ... Implement annotation,structured feedback loops,fine-tuning, and alignment methods to calibrate ...

Roles & Responsibilities o Implement Guardrails and observability across RAG and LLM applications o ... Implement annotation,structured feedback loops,fine-tuning, and alignment methods to calibrate ...

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How much do llm annotation jobs pay per year?

As of May 31, 2026, the average yearly pay for llm annotation in the United States is $40,000.00, according to ZipRecruiter salary data. Most workers in this role earn between $40,000.00 and $40,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an LLM Annotation Specialist, and why are they important?

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 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 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.

More about Llm Annotation jobs
What cities are hiring for Llm Annotation jobs? Cities with the most Llm Annotation job openings:
What states have the most Llm Annotation jobs? States with the most job openings for Llm Annotation jobs include:
Senior Software Engineer - LLM Trainer

Senior Software Engineer - LLM Trainer

Kake

Remote

$125.40K - $165.30K/yr

Full-time

Posted 5 days ago


Job description

Job Summary:
Kake is seeking a Senior Software Engineer to contribute to the development and evaluation of AI training data for AI agents and LLMs. In this role, you will work at the intersection of software engineering and artificial intelligence, helping to build better AI models by leveraging your technical expertise.
Responsibilities:
• Create and review coding tasks based on real-world software engineering scenarios, including debugging, refactoring, code generation, API usage, automated tests, performance, security, and edge cases
• Write high-quality reference solutions that are correct, clear, testable, and aligned with task requirements
• Evaluate AI-generated code and responses using structured rubrics, assessing correctness, clarity, security, performance, maintainability, and instruction-following
• Compare multiple model responses, select the strongest answer, and justify your decision with clear technical reasoning
• Identify bugs, hallucinated APIs, missing edge cases, weak explanations, and poor engineering decisions in AI-generated outputs
• Work with terminal-based development workflows when needed, including running tests, debugging issues, managing dependencies, and navigating repositories
• Follow detailed guidelines consistently and participate in calibration activities to ensure high-quality, reliable evaluations
Qualifications:
Required:
• 5+ years of professional software engineering experience in a backend, fullstack, or systems role
• Strong proficiency in at least one core programming language, ideally Python, JavaScript/TypeScript, Go, Java, C++, or SQL
• Hands-on experience with Terminal-Bench, with the ability to evaluate AI agent performance on terminal-based tasks including compiling code, running tests, managing environments, and completing multi-step software engineering workflows
• Comfortable working with Git, command line/terminal, and common development workflows
• Ability to evaluate code critically - not only whether it works, but whether it is well-designed, secure, and maintainable
• Prior experience in AI data production, RLHF, data annotation, or LLM evaluation projects
• Excellent written and verbal communication skills in English
• Ability to work independently in a remote, asynchronous, fast-paced environment
• High attention to detail and the ability to follow complex, rubric-based guidelines consistently
Preferred:
• Experience with Python-heavy workflows, automated testing frameworks, Docker, Linux, bash, or containerized environments
• Experience with repo-level code reasoning, large codebases, or open-source contributions
• Background in backend systems, data engineering, DevOps, infrastructure, security, or large codebase
Company:
Life is Better with Kake! Kake offers premier teams of software engineers to support some of the world's most prominent and innovative brands. Founded in , the company is headquartered in Austin, TX, US, , with a team of 201-500 employees. The company is currently Growth Stage.