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Meta Data Labeling Jobs (NOW HIRING)

... content labeling and analysis * 1+ years working with GenAI products, prompt engineering ... Experience in data visualization and tools such as Tableau About Meta: Meta builds technologies ...

... content labeling and analysis * 1+ years working with GenAI products, prompt engineering ... Experience in data visualization and tools such as Tableau About Meta: Meta builds technologies ...

... labeling and analysis • Expertise in developing and implementing high-quality datasets ... in data visualization and tools such as Tableau About Meta Meta builds technologies that help ...

... labeling and analysis • Expertise in developing and implementing high-quality datasets ... in data visualization and tools such as Tableau About Meta Meta builds technologies that help ...

NE

$150K/yr

Meta is seeking a Regional Quality Manager to join our Data Center Design, Engineering ... Provide assistance in reviewing installed labeling, signage and tagging * Attend required ...

Product Content Engineer

Menlo Park, CA · On-site

$162K - $227K/yr

... and/or content labeling and analysis • Experience designing and implementing evaluation ... in Computer Science, Data Science, Linguistics, or related field About Meta Meta builds ...

Meta is seeking a Regional Quality Manager to join our Data Center Design, Engineering ... Provide assistance in reviewing installed labeling, signage and tagging * Attend required ...

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Meta Data Labeling information

What are the key skills and qualifications needed to thrive as a Meta Data Labeling Specialist, and why are they important?

To thrive as a Meta Data Labeling Specialist, you need strong attention to detail, data literacy, and familiarity with data annotation principles, often supported by a background in computer science or related fields. Proficiency with data labeling platforms, annotation tools, and sometimes basic programming or scripting is typically required. Excellent communication, consistency, and problem-solving skills help ensure accuracy and effective collaboration with data science teams. These skills are crucial for producing high-quality labeled data, which is foundational for training reliable machine learning models.

What are some common challenges faced by professionals in Meta Data Labeling roles, and how can they be addressed?

Professionals in Meta Data Labeling often encounter challenges such as handling large volumes of data, maintaining consistency in labeling, and adapting to evolving project guidelines. To address these, it’s important to follow clear labeling protocols, regularly communicate with team leads or project managers, and participate in quality assurance checks. Collaborative tools and regular feedback sessions can also help ensure accuracy and stay aligned with project objectives, making teamwork and adaptability essential skills in this environment.

What is meta data labeling?

Meta data labeling is the process of assigning descriptive tags or labels to data, such as images, text, or videos, to make it easier for machine learning models to understand and process the information. This task is crucial for training artificial intelligence systems, as labeled data helps algorithms learn to identify patterns and make accurate predictions. Meta data labeling can involve categorizing objects, annotating features, or adding contextual information, depending on the specific needs of the project. Accurate labeling ensures that AI systems perform reliably and efficiently.

What does a data labeling analyst do at Meta?

A data labeling analyst at Meta is responsible for reviewing and annotating data such as images, videos, and text to improve machine learning models. They use specialized tools and follow guidelines to ensure data accuracy and consistency, often working in a collaborative environment with quality control measures.

What is the difference between Meta Data Labeling vs Data Annotator?

AspectMeta Data LabelingData Annotator
Primary FocusLabeling metadata such as tags, categories, or attributes for data setsAnnotating raw data like images, text, or audio for training AI models
Skills & CertificationsBasic understanding of data structures, attention to detailStrong annotation skills, familiarity with annotation tools
Work EnvironmentData labeling platforms, remote or office-basedAnnotation tools, remote or office-based
Industry UsageData management, AI training, machine learningAI development, machine learning, data science

Meta Data Labeling involves assigning descriptive tags or attributes to data, helping organize and categorize datasets. Data Annotator focuses on marking or labeling raw data to train AI models. While both roles support AI and machine learning, Meta Data Labeling emphasizes metadata management, whereas Data Annotator concentrates on raw data annotation.

Infographic showing various Meta Data Labeling job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Founding Account Executive - ~$350K OTE - Bay Area - AI Expert Services & Training Data - Remote US

Founding Account Executive - ~$350K OTE - Bay Area - AI Expert Services & Training Data - Remote US

RevsUp

San Mateo, CA • Remote

$350K/yr

Other

Medical, Retirement

This job post has expired today. Applications are no longer accepted.


Job description

Opportunity Overview – Founding AE, AI Expert Services (remote)


A growth-stage AI data company is hiring a Founding AE for its AI Expert Services vertical, a new revenue line selling subject-matter-expert (SME) services and human-generated training data into ~60–70 frontier AI labs and AI-native companies building foundation models (OpenAI, Anthropic, Google DeepMind, Meta, and peers). This is a quota-carrying, 0→1 individual contributor role for a builder who can create the GTM motion from scratch, land initial reference accounts, and expand a concentrated named-account territory into a repeatable $2M+ book of business.


A growth-stage AI data company is a ~100-employee, PE-backed AI data company recently acquired by a ~10,000-employee global platform. This role combines founder-level ownership with the infrastructure and scale of an established parent, operating within a highly concentrated TAM with only a handful of credible competitors. Fully remote in the US, W2 with full benefits, reporting to the SVP and Co-Founder.


Solution

The AI Expert Services vertical sells vetted, domain-expert human labor to frontier AI labs and AI-native builders. SMEs with deep fintech, healthcare, legal, and other regulated-industry expertise generate the post-training prompts, answers, reasoning traces, and evaluations that leading foundation models need to reach expert-level performance in high-stakes domains.


Unlike broad data-labeling vendors, AI Expert Services is a concentrated, high-margin offering: the company sources, vets, and manages credentialed SMEs (MDs, CPAs, JDs, PhDs, senior practitioners), then deploys them against model-training tasks billed on an hourly basis. The company’s existing platform, global operations footprint, and in-house contributor network of 500,000+ give this new vertical immediate scale advantages over the 6–7 competitors targeting the same TAM.

How the Business Works


AI Expert Services is an hourly, services-based business, not SaaS. Customers engage the company to stand up pods of credentialed SMEs against specific model-training workstreams, then expand scope as new domains, modalities, and evaluation needs emerge. Revenue is driven by landing initial projects, proving quality and throughput, and growing hours-per-account over time rather than by recurring subscriptions.


Role

• Founding, quota-carrying Individual Contributor reporting to the SVP and Co-Founder. Fully remote across the US with access to the San Francisco office. Travel required for key customer meetings, conferences, and strategic accounts.

• ~$350K OTE at a 40/60 base-to-variable split with a $2M quota. Uncapped comp designed to reward landing and expanding initial AI Expert Services accounts. W2 with full benefits including 401k and health plan. No equity due to PE-backed structure.

• Deal sizes range from $250K to $2M+ with meaningful expansion potential as SME pods scale. Hourly, services-based model where customers expand scope across domains and use cases. You own initial land and deep account expansion.

• First dedicated AE for the AI Expert Services vertical. You will build the GTM motion from scratch and own a concentrated territory of ~60–70 frontier AI labs and AI-native companies (OpenAI, Anthropic, Google DeepMind, Meta, etc.).

• Primary buyers include post-training leads, research engineers, heads of data, and AI product leaders. Pipeline is driven through targeted outbound, warm intros, and relationship-based selling into a tight buyer community. This is a consultative, technical sale where credibility matters.

• 8 to 10+ years of enterprise B2B sales experience closing $250K–$2M+ deals into AI-native or highly technical buyers. Direct experience selling expert services, RLHF, post-training data, or similar offerings is strongly preferred. Not a fit for SaaS-only or data-labeling-only backgrounds.

• Not for SMB or mid-market sellers, high-volume transactional reps, or candidates looking to learn AI.

• Priority hire. You will define the playbook, land initial reference logos, and build the foundation for the next wave of sellers.


Why Join

• Founding AE for a brand-new AI Expert Services vertical, defining the playbook end-to-end

• Founder-level ownership with the backing and infrastructure of a 10,000+ employee global platform

• Selling into the ~60–70 frontier AI labs building the world’s most important foundation models

• Uncapped comp (~$350K OTE, 40/60) with direct reporting line to the SVP and Co-Founder


Culture

• Acquired by a global services platform in February 2026, bringing the scale of a ~10,000-employee, PE-backed parent. The company operates independently with its own P&L and maintains a startup-oriented, agile culture.

• Co-Founder and hiring manager has 15+ years scaling startups in edtech and compliance.

• Rated 4.6/5 on Glassdoor (80 reviews, 92% recommend) and 4.3/5 on G2. Recognized with awards including Best AI Training Data Vendor (2023), Global AI Summit Award for Conversational AI, and Stevie Awards for Most Innovative Tech Startup.

• “The platform gave me access to top-tier pre-trained vision, NLP, and speech models with a simple, intuitive interface.” – G2 Review


Official Job Description


A growth-stage AI data company is hiring a Founding, quota-carrying Account Executive to launch and scale the AI Expert Services vertical across the US market. This is a 0→1 role owning pipeline development, the first reference accounts, and expansion of a concentrated named-account territory of ~60–70 frontier AI labs and AI-native builders. The Founding AE sells vetted subject-matter-expert (SME) services and human-generated training data on an hourly, services-based model into the teams building the world’s most important foundation models.


Key Responsibilities:

• Build the AI Expert Services GTM motion from scratch. Own the full enterprise sales lifecycle, from pipeline generation through close, across a named-account territory of ~60–70 frontier AI labs and AI-native companies building foundation models.

• Build trusted relationships with post-training leads, alignment teams, research engineers, and heads of data; translate customer model-training requirements (RLHF, evaluations, reasoning traces, domain expert prompts and answers) into structured SME pod engagements alongside internal solution and delivery teams. Land initial engagements, prove quality and throughput, and expand hours-per-account into seven-figure run rates.


Required Experience:

• 8–10+ years of enterprise B2B sales with a track record of closing $250K–$2M+ deals and demonstrated quota achievement at or above $2M. Comfort operating as a founding seller building a motion from scratch.

• Direct experience selling expert services, RLHF, post-training data, model evaluations, or adjacent AI model-training offerings into frontier AI labs or AI-native builders. Sellers from pure SaaS or data-labeling-only backgrounds will not be a fit.

Preferred Experience:

• Deep familiarity with frontier AI post-training workflows, RLHF pipelines, reasoning and evaluation data, and domain SME engagements (fintech, healthcare, legal, regulated industries). Experience selling into OpenAI, Anthropic, Google DeepMind, Meta, Mistral, or peer labs. Comfortable with globally distributed SME delivery teams.

• Tools: Salesforce, HubSpot, LinkedIn Sales Navigator, Apollo (or similar).


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About RevsUp

Sourced by ZipRecruiter

Industry

Recruiting and staffing services

Company size

51 - 200 Employees

Headquarters location

Scottsdale, AZ, US

Year founded

2015