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Llm Annotation Jobs (NOW HIRING)

Kake

Senior Software Engineer - LLM Trainer

$125K - $165K/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 ...

Apple

Data Scientist

Sunnyvale, CA

$184K - $324K/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.

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)

Sourceability

Principal NLP Scientist

Austin, TX · On-site

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 ...

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Llm Annotation information

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$11K

$41.5K

How much do llm annotation jobs pay per year?

As of Aug 22, 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 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
Infographic showing various Llm Annotation job openings in the United States as of August 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 77% Physical, 6% Hybrid, and 17% Remote job distribution, with an average salary of $40,000 per year, or $19.2 per hour.

Software Engineer, AI Agent & LLM

Otter.ai

Mountain View, CA • On-site, Remote

$155K - $185K/yr

Full-time

Re-posted 17 hours ago

Job description

The Opportunity

We are looking for a Senior AI Agent & LLM Engineer who combines strong software engineering capabilities with a deep focus on AI quality. You will help build and improve the AI systems behind Otter's conversational knowledge engine and AI Chat, spanning user-facing agent experiences, evaluation systems, and the shared platform required to operate them reliably at scale.

This is a hands-on role for someone who can move from an ambiguous product problem to a working production solution, define how quality should be measured, and drive improvements across services, models, and user experience.

Your Impact
  • Build AI-agent experiences for Otter AI Chat that help users reason over conversations, retrieve knowledge, and complete complex tasks.
  • Advance Otter's conversational knowledge engine through better knowledge extraction, annotation, and indexing.
  • Develop robust evaluation datasets, automated graders, regression tests, and release gates for AI quality.
  • Diagnose failures across models, prompts, retrieval, tools, data pipelines, backend services, and product workflows.
  • Build shared agent infrastructure for orchestration, tracing, debugging, retries, sandboxed execution, and observability.
  • Improve task completion, correctness, groundedness, reliability, latency, and cost.
  • Turn production traces, customer feedback, and usage signals into measurable product and model improvements.
  • Collaborate across AI, product, infrastructure, data, security, and application teams to deliver end-to-end capabilities.
We're looking for someone who
  • Has 3+ years of AI Agent engineering, machine learning engineering, or related experience.
  • Brings strong backend or distributed-systems engineering skills.
  • Has hands-on experience building and shipping AI-agent or LLM-powered products.
  • Has a strong focus on AI quality and experience evaluating nondeterministic systems.
  • Can use data, traces, logs, and qualitative examples to identify and resolve complex failures.
  • Works effectively across services, technical domains, and organizational boundaries.
  • Combines strong product judgment with rigorous engineering and evaluation practices.
  • Operates with high agency, strong ownership, and a bias toward action.
  • Can take ambiguous problems from initial exploration through production launch and continuous improvement.
  • Demonstrated ability to use coding agents effectively while rigorously reviewing and controlling the quality of their output is a plus.
About Otter.ai

We are in the business of shaping the future of work. Our mission is to make conversations more valuable.

With over 1B meetings transcribed, Otter.ai is the world's leading tool for meeting transcription, summarization, and collaboration. Using artificial intelligence, Otter generates real-time automated meeting notes, summaries, and other insights from in-person and virtual meetings - turning meetings into accessible, collaborative, and actionable data that can be shared across teams and organizations. The company is backed by early investors in Google, DeepMind, Zoom, and Tesla.

Otter.ai is an equal opportunity employer. We proudly celebrate diversity and are committed to building an inclusive and accessible workplace.  We provide reasonable accommodations for qualified applicants throughout the hiring process. 

Accessibility & Accommodations 

Otter.ai is committed to providing reasonable accommodations for candidates with disabilities in our hiring process.  If you need assistance or an accommodation during any stage of the recruitment process, please contact hr@otter.ai at least 3 business days before your interview.

*Otter.ai does not accept unsolicited resumes from 3rd party recruitment agencies without a written agreement in place for permanent placements. Any resume or other candidate information submitted outside of established candidate submission guidelines (including through our website or via email to any Otter.ai employee) and without a written agreement otherwise will be deemed to be our sole property, and no fee will be paid should we hire the candidate.

Salary range

Salary Range: $155,000 to $185,000 USD per year

This salary range represents the low and high end of the estimated salary range for this position. The actual base salary offered for the role is dependent based on several factors. Our base salary is just one component of our comprehensive total rewards package.
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