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Llm Annotation Jobs (NOW HIRING)

Mill

AI Engineer, Computer Vision

San Bruno, CA · On-site

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 ...

Mill

AI Engineer, Computer Vision

San Bruno, CA

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 ...

CreatorIQ, Inc

ML Engineer

New York, NY · 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 ...

Welocalize, Inc.

OR

$16 - $20.75/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 ...

Welocalize, Inc.

Data Quality Analyst

New York, NY

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 ...

Welocalize, Inc.

OR

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 ...

Welocalize, Inc.

Data Labeling Associate

New York, NY

$17.50 - $22.75/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 ...

Welocalize, Inc.

Data Quality Analyst

San Francisco, CA · On-site

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 ...

Welocalize, Inc.

OR

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 ...

Sigma Group

Tamil Translator (Remote) | Sigma AI

$45K - $58.90K/yr

Experience with one or more of the following: computational linguistics, corpus analysis, language data annotation, or LLM training * Strong attention to detail What will you do? Annotation - Audio ...

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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.

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What job categories do people searching Llm Annotation jobs look for? The top searched job categories for Llm Annotation jobs are:
Llm Annotation jobs near you

AI Engineer, Computer Vision

Mill

San Bruno, CA • On-site

Full-time

Posted 24 days ago

Job description

Mill is a waste prevention technology company reimagining what it means to eliminate waste, starting with food. We build smart systems and infrastructure for homes, businesses, and municipalities that transform food scraps from landfill-bound waste into valuable resources, including chicken feed. Tens of thousands of Mill's residential food recyclers are already helping households divert millions of pounds of food scraps every year, paving the way for our upcoming launch of Mill Commercial-the industry's first end-to-end solution for managing, understanding, and preventing food waste in commercial environments (e.g. grocery, restaurants, food services). At Mill, we are passionate about building easy-to-use, beautifully designed technologies that keep food in the food system and out of landfills.
About the Role
We're hiring an AI Engineer to work on the AI core of Mill Commercial - the computer vision and agentic systems that turn a stream of food waste into operational intelligence for commercial kitchens. Mill Commercial integrates a camera and onboard compute directly into our high-capacity food recycler; models running on the edge identify, classify, and quantify food scraps at the point of generation, and our vision pipeline turns that signal into procurement and operational guidance for large food service operators.
You'll join a small AI team, building the data and training pipeline that produces our edge CV models, designing the cloud-side evaluation that tells us whether those models are good enough to ship, and helping build the agentic, LLM-driven product features that turn raw waste data into customer-facing insights and recommendations. This is a hands-on senior IC role for someone who's equally comfortable fine-tuning a segmentation model, prompting a VLM, and wiring an agent into a product feature.
What You'll Do
  • Build and manage the end-to-end ML training pipeline: data ingestion from deployed kitchen units, ground truth generation, annotation tooling (including foundation-model-assisted labeling), training, evaluation, and retraining cycles.
  • Train and evaluate segmentation, classification, and mass-estimation models for the Mill Commercial camera pipeline - from prompting foundation models to fine-tuning ConvNets and VLMs.
  • Build the cloud-side evaluation harness that tells us how our shipped edge models are actually performing in the field - automated, reproducible, and aligned to product accuracy targets across food types, kitchen environments, and deployment configurations.
  • Own MLOps: reproducible training, experiment tracking, model versioning, and automated evaluation against product-defined accuracy targets.
  • Export and validate models for deployment to edge devices, working closely with the edge team on optimization, quantization, and integration.
  • Help design and build the LLM- and agent-powered product features that consume waste characterization data and turn it into customer-facing recommendations - purchasing suggestions, anomaly explanations, operational nudges. Define how agents call tools, ground in customer data, and stay reliable in production.
  • Analyze failure cases systematically - unfamiliar food classes, novel kitchen environments, challenging lighting and clutter conditions - and drive the data and modeling decisions that close accuracy gaps.
What We're Looking For
  • Strong fundamentals in computer vision and deep learning - segmentation, detection, classification, tracking. You understand the architectures well enough to make informed choices.
  • Fluency with modern ML approaches - VLMs, LLMs, foundation models, and agentic systems - alongside classical deep learning. You know when to fine-tune a ConvNet, when to prompt a VLM, and when to wire up an agent, and you understand the practical realities of putting any of them into a product.
  • Experience building ML training pipelines and data annotation systems at scale.
  • Experience evaluating ML models rigorously - designing metrics, building the eval harness, and using results to drive product decisions rather than just publish a number.
  • Proficiency with cloud ML infrastructure (AWS or equivalent) - you've managed training jobs, data pipelines, and experiment workflows in production.
  • Familiarity with cloud-to-edge model deployment.
  • Clear, direct communication - you can explain tradeoffs to non-technical stakeholders, push back honestly when you disagree, and write docs that others can follow.
  • Genuine interest in applying AI to food waste reduction and sustainability. This is a mission-driven product and we want people who care about the mission.

Software skills: Python, PyTorch, OpenCV. Strong familiarity with MLOps on AWS infrastructure. Experience with LLM and agent frameworks. Google Cloud / Gemini experience is a plus.
Nice to Have
  • Experience with video understanding (temporal consistency, tracking, video segmentation)
  • 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 consumer or B2B product
  • Hardware / IoT product experience, particularly with computer vision and cameras for embedded systems

The estimated base salary range for this position is $240 to $280k, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs. At Mill, it is not typical for an individual to be hired at or near the top of the range for their role.

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