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Dataannotation Jobs in California (NOW HIRING)

General Motors

Senior ML Systems Engineer

Sunnyvale, CA

$122K - $168K/yr

We own a modern fullstack architecture including TypeScript/React, Python, GraphQL, Golang , and ML model services , which powers dataannotation pipelines and machineled training data solutions at ...

General Motors

ML Systems Engineer

Mountain View, CA · On-site

... dataannotation pipelines and MLled labeling solutions at foundationmodel scale . We partner closely with ML engineers , Operations , Product Management , Data Science , and other ML Platform groups.

General Motors

ML Systems Engineer

Sunnyvale, CA · On-site

... dataannotation pipelines and MLled labeling solutions at foundationmodel scale . We partner closely with ML engineers , Operations , Product Management , Data Science , and other ML Platform groups.

General Motors

Staff ML Systems Engineer

Sunnyvale, CA · On-site

We own a modern fullstack architecture including TypeScript/React, Python, GraphQL, Golang , and ML model services , which powers dataannotation pipelines and machineled training data solutions at ...

All JobsDataannotation JobsDataannotation Jobs in California

Dataannotation information

What is a dataannotation?

Data annotation jobs involve labeling or tagging data—such as text, images, audio, or video—to help train machine learning models. Annotators review raw data and add relevant information or classifications, such as identifying objects in a photo or transcribing spoken words. This work is essential for improving the accuracy of artificial intelligence systems. Data annotation can be performed manually or with the assistance of specialized software, and the tasks can range from simple to complex depending on the project requirements.

What skills and qualifications are needed to thrive as a dataannotation?

To thrive as a Data Annotation Specialist, you need strong attention to detail, consistency, and a basic understanding of data labeling concepts, often supported by a high school diploma or relevant experience. Familiarity with annotation tools such as Labelbox, Supervisely, or CVAT, and sometimes basic knowledge of data formats and guidelines, is typically required. Excellent focus, time management, and the ability to follow detailed instructions help individuals excel in this role. These skills ensure the production of high-quality labeled datasets that are crucial for effective machine learning and AI model development.

What are common challenges faced by dataannotation professionals, and how can they be managed effectively?

Data Annotation professionals often encounter challenges such as maintaining high accuracy while labeling large volumes of data and staying consistent with annotation guidelines across different projects. To manage these challenges, it's important to regularly communicate with team members, seek clarification on ambiguous cases, and utilize quality assurance processes like peer reviews. Many teams use specialized annotation tools and periodic training sessions to help annotators stay aligned and efficient. Developing strong attention to detail and adaptability also helps tackle the evolving requirements of data annotation tasks.

What is the difference between Dataannotation vs Data Analyst?

AspectDataannotationData Analyst
Required credentialsTypically high school diploma or equivalent; some roles may require basic technical skillsBachelor's degree in data science, statistics, or related field
Work environmentData labeling or annotation platforms, remote or on-siteOffice setting, often collaborative, with data analysis tools
Employer and industry usageTech companies, AI development, machine learning projectsBusiness, finance, healthcare, tech industries
Common search and comparison intentUnderstanding entry-level data roles, technical skills neededAnalyzing data, generating insights, decision-making

Dataannotation involves labeling data for machine learning, requiring basic technical skills and often minimal formal education. Data analysts interpret data to inform business decisions, requiring more advanced analytical skills and a degree. While both roles work with data, data annotation is more focused on preparing data, whereas data analysis involves deriving insights from data sets.

Does data annotation really pay you?

Data annotation jobs typically pay hourly or per task rates, with earnings varying based on experience, complexity of the data, and platform. Many data annotators earn between minimum wage and higher, depending on skill level and the employer, often working remotely with flexible schedules. Payment is usually processed through online platforms or direct deposits after completing assigned tasks.

How hard is it to get hired by data annotation?

Getting hired as a data annotator generally requires basic computer skills, attention to detail, and sometimes familiarity with specific tools or platforms. Many positions are entry-level and may not require formal certifications, but a reliable internet connection and the ability to follow instructions are important. Competition can vary depending on the company and location, but opportunities are often available for those with the necessary skills and a flexible schedule.

What cities in California are hiring for Dataannotation jobs?

Cities in California with the most Dataannotation job openings:

Dataannotation jobs near you
Infographic showing various Dataannotation job openings in California as of August 2026, with employment types broken down into 43% Full Time, and 57% Contract. Highlights an 20% Physical, and 80% Remote job distribution.

Strategic Projects Lead - Audio Data

Success Matcher Recruitment

Redwood City, CA • Hybrid

$63.25 - $85.75/hr

Full-time

Re-posted 11 days ago

Job description

About the Company

Our client is building the foundational data and benchmark infrastructure for next-generation Voice AI, helping frontier AI labs train models to understand humanity across every language, dialect, and accent.

Backed by Y Combinator and over $3.5M in seed funding from top-tier Silicon Valley investors (including surgepoint, Amino Capital, and co-founders of Twitch and Cruise), they operate as a lean, elite team of ~10 scaling rapidly in the Bay Area.

About the Role

As the Strategic Projects Lead - Audio Data, you will own and deliver high-priority audio data collection projects end-to-end. In this high-ownership, ground-floor role, you will be the single-threaded owner of large-scale, six-figure+ contracts, driving messy and complex projects from customer kickoff through to final, high-quality delivery.

What You Will Do

  • Project Delivery: Manage complex audio data collection projects, overseeing contributor sourcing, engagement, quality assurance, and client communication.
  • Quantitative Operations: Track project progress and KPIs quantitatively. You will build dashboards, analyze throughput/quality data, and proactively solve operational bottlenecks.
  • Client Interface: Work directly with leading frontier AI labs to clarify requirements, deliver progress updates, and iterate based on their feedback.
  • Product Collaboration: Propose and drive technical and product changes with the engineering team to improve platform capabilities, contributor workflows, and QA tooling.
  • Resource Management: Hire and manage external QA resources as needed, collaborating directly with the co-founders and engineers to unblock delivery.

What You Bring

  • Experience: 2 to 7 years of data operations experience specifically on the supply side at a data annotation/labeling vendor company (e.g., Scale AI, Surge AI, Appen, Micro1, TELUS Digital, DataAnnotation, or a similar AI data startup).
  • Track Record: Proven success delivering six-figure+ data collection or annotation contracts end-to-end.
  • Technical Domain: Direct experience managing audio, speech, voice, or multimodal data projects.
  • Analytical Chops: Strong analytical skills with the ability to build dashboards and use data to solve quantitative operational problems.
  • QA Design: Quality workflow design experience, including creating annotation guidelines, QA rubrics, and quality control systems.
  • Mindset: Extreme ownership, resilience in the face of ambiguity, and a habit of pushing through obstacles to deliver results.
  • Presence: Ability to work full-time on-site (5 days/week) in the San Mateo/Redwood City, CA area.