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Label Studio Jobs (NOW HIRING)

HumanSignal

Technical Program Manager

San Francisco, CA

$152K - $196.80K/yr

We design and create datasets from scratch, recruit and manage the domain experts who evaluate model output, and run everything through our own platform, Label Studio, the open-source standard for ...

Create Music Group

Studio Intern

Los Angeles, CA · On-site

Create Music Group is an LA-based digital music company that provides artists and labels with ... Job Summary The Create Music Group Studios Internship program was designed to give participants ...

Ivy Tech

Studio and Gallery Technician

Indianapolis, IN · On-site

$18 - $20/hr

Job Title: Studio and Gallery Technician Location: Indianapolis - Downtown Campus Job Type ... Create labels and other didactic materials as needed * As required, assist with preparation and ...

HumanSignal

Director of Finance

San Francisco, CA

About the Role We're hiring a Director of Finance to own the financial backbone of HumanSignal, the company behind Label Studio. Reporting directly to the CEO, you'll own financial planning, business ...

Aston Carter

Studio Prep Assistant

Brooklyn, NY · On-site

$20/hr

... • Clean workspace and studio supplies. • Repackage items daily after they have been ... Labelling Top Skills Details E-commerce,Fulfillment,inventory Additional Skills & Qualifications ...

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Label Studio information

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$28K

$53.4K

$77.5K

How much do label studio jobs pay per year?

As of Jun 4, 2026, the average yearly pay for label studio in the United States is $53,399.00, according to ZipRecruiter salary data. Most workers in this role earn between $42,000.00 and $60,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Label Studio Data Annotation Specialist, and why are they important?

To excel as a Label Studio Data Annotation Specialist, you need a solid understanding of data labeling concepts, attention to detail, and experience with data annotation processes, often supported by familiarity with machine learning workflows. Proficiency in using the Label Studio platform, knowledge of data formats like JSON and CSV, and occasionally scripting skills in Python are valuable technical assets. Strong communication, teamwork, and problem-solving abilities help you interpret guidelines and collaborate with data science teams. These skills ensure high-quality, consistent labeled data, which is critical for training accurate machine learning models.

What are some common challenges faced when working as a Label Studio annotator, and how can they be addressed?

One frequent challenge in a Label Studio role is ensuring consistent and accurate data annotation, especially when dealing with ambiguous or complex data. Annotators often need to interpret guidelines carefully and collaborate closely with team members to resolve uncertainties. Regular communication with project managers, participation in calibration sessions, and thorough review of annotation instructions can help maintain high-quality output. Additionally, using Label Studio’s built-in collaboration and review features streamlines feedback and quality control, making it easier to address inconsistencies as a team.

What is Label Studio?

Label Studio is an open-source data labeling tool that enables users to annotate various types of data, including images, text, audio, and videos. It is widely used for preparing training datasets for machine learning and artificial intelligence applications. Label Studio supports customizable labeling interfaces, collaborative annotation workflows, and integrates easily with other data science tools. Its flexibility and extensibility make it a popular choice for both individual researchers and enterprise teams.

What is the difference between Label Studio vs Data Annotator?

AspectLabel StudioData Annotator
Required CredentialsBasic technical skills, familiarity with annotation toolsTypically high school diploma or equivalent, on-the-job training
Work EnvironmentSoftware platform, remote or on-siteOffice or remote, depending on employer
Industry UsageData labeling for AI/ML projects across various industriesData annotation tasks within organizations or outsourcing firms
Common Search IntentTools for data labeling, annotation softwareJob roles in data annotation, entry-level labeling jobs

Label Studio is a versatile data labeling tool used by professionals to create training data for AI models, while Data Annotator refers to the role of performing data labeling tasks, often as an entry-level position. Both are integral to AI development, but Label Studio is a software platform, whereas Data Annotator is a job role.

More about Label Studio jobs
What cities are hiring for Label Studio jobs? Cities with the most Label Studio job openings:
What states have the most Label Studio jobs? States with the most job openings for Label Studio jobs include:
What job categories do people searching Label Studio jobs look for? The top searched job categories for Label Studio jobs are:
Label Studio jobs near you
Infographic showing various Label Studio job openings in the United States as of May 2026, with employment types broken down into 67% Full Time, and 33% Temporary. Highlights an 100% In-person job distribution, with an average salary of $53,399 per year, or $25.7 per hour.

Technical Program Manager

HumanSignal

San Francisco, CA

$152K - $196.80K/yr

Full-time

Posted 6 days ago

Job description

About HumanSignal

Real-world data is the competitive edge in AI.

HumanSignal is a human data partner for companies building AI models and products. Our customers ship better AI, faster, because we partner with their researchers from real-world data creation to annotation to delivery.


We design and create datasets from scratch, recruit and manage the domain experts who evaluate model output, and run everything through our own platform, Label Studio, the open-source standard for data labeling and evaluation, used by over 1 million practitioners worldwide.


We specialize in the operationally complex: real-world data collection, multimodal pipelines, and multi-step workflows. Advanced ML and AI teams use our enterprise platform to run their own data factories, and our services team to extend their reach where in-house capacity runs out.


If you want to do work that materially shapes how the next generation of AI products gets built, we'd love to talk.

What You'll Do
  • Translate customer outcomes into integrated delivery plans — milestones, dependencies, critical path, DRIs, definitions of done — and manage scope as engagements evolve
  • Own delivery health end-to-end: maintain crisp status, surface risks early, and drive blockers to resolution so engineers stay focused on technical work
  • Run tight cadences that hold teams accountable to outcomes without micromanagement; every open item has an owner and a due date
  • Build durable delivery systems: single source of truth, action-item registers, risk registers, and playbooks that scale across the delivery org
  • Draft regular customer updates (often daily), own live and async reporting, and maintain stakeholder confidence through proactive communication
  • Identify automation opportunities and build AI-driven workflows to reduce operational overhead across engagements
  • Partner closely with Product, Engineering, and Go-To-Market teams to continuously raise the bar on delivery excellence
Required Qualifications
  • 8+ years in program/technical program management or delivery operations
  • Track record managing complex, multi-stakeholder enterprise programs
  • Metrics-driven: owns and reviews delivery health KPIs weekly (on-time milestone rate, blocker aging, schedule variance, etc.)
  • Strong judgment under pressure; makes good decisions with incomplete information
  • Confident presence with senior engineers and executive customer stakeholders
Preferred Qualifications
  • Experience with enterprise AI or ML deployments
  • Familiarity with enterprise IT procurement and security processes
  • Background in startup ops, management consulting, or forward-deployed engineering
  • Experience building lightweight operating systems for distributed teams