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Data Annotation Research Jobs in California (NOW HIRING)

Welocalize, Inc.

Data Labeling Associate

San Diego, CA · On-site

$17 - $22/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... User Experience Research team. * Implement basic quality control measures and ensure the ...

Socket.dev

Research Engineers, Data

San Francisco, CA · On-site

$150 - $250/hr

We research and deploy technologies that power AI-native operations -- both for our partners and ... Develop synthetic data, annotation, and feedback‑loop strategies to improve system performance in ...

Specter

Data Operations Engineer

San Francisco, CA · On-site

$81K - $110K/yr

Role: Specter is hiring a data operations engineer to build our research data operation. This ... Build and maintain internal tooling for labelers, including annotation interfaces, task pipelines ...

Turing

Client Director, Frontier Data - US

Palo Alto, CA

$255K - $325K/yr

Oversee the entire data lifecycle from client intake and annotation workflow design to delivery * Partner with product, research, and engineering teams to implement evaluation metrics (e.g., win rate ...

campus4tech

Technical Program Manager III

Mountain View, CA · On-site

$152K - $197K/yr

The Client's R&D Operations Organization is seeking a highly motivated and technically skilled Technical Program Manager (TPM) to lead and oversee data annotation programs that power our cutting-edge ...

Transformcap

Clinical Data Abstractor

San Mateo, CA · On-site

$80 - $100/hr

Work collaboratively within the clinical annotation team to maintain consistency in data extraction approaches and ensure delivery commitments are met for all research partners. * Demonstrate ...

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All JobsData Annotation Research JobsData Annotation Research Jobs in California

Data Annotation Research information

What is data annotation research?

Data annotation research involves studying and developing methods for labeling data, such as images, text, or audio, to be used in training machine learning models. Researchers in this field focus on improving annotation accuracy, efficiency, and scalability, as well as addressing challenges like bias and consistency. This work is critical because high-quality annotated data is essential for building effective AI systems. Data annotation research often includes exploring new tools, techniques, and guidelines for human annotators or automated labeling systems.

What are the key skills and qualifications needed to thrive as a data annotation researcher, and why are they important?

To thrive as a Data Annotation Researcher, you need strong attention to detail, analytical thinking, and familiarity with data labeling concepts, often supported by a degree in computer science, linguistics, or a related field. Experience with annotation platforms, data management tools, and sometimes knowledge of programming languages like Python are typically required. Excellent communication, problem-solving abilities, and the capacity to work independently set standout contributors apart. These skills ensure high-quality, accurate data labeling, which is crucial for developing reliable AI and machine learning models.

What are some common challenges faced in data annotation research roles, and how can they be addressed?

Professionals in Data Annotation Research often encounter challenges such as maintaining consistency in labeling, dealing with ambiguous data, and managing large datasets efficiently. These issues can be addressed by following detailed annotation guidelines, participating in regular calibration sessions with the team, and utilizing annotation tools that support quality control checks. Collaboration with data scientists and project managers is essential to clarify ambiguities and ensure that annotated data meets the project's requirements. Staying proactive in communication and continuous learning helps to minimize errors and improve overall data quality.

What is the difference between Data Annotation Research vs Data Labeling Specialist?

AspectData Annotation ResearchData Labeling Specialist
CredentialsTypically requires a background in data science, research methods, or related fieldsOften requires basic technical skills and experience with labeling tools
Work EnvironmentResearch labs, tech companies, or remote research teamsData centers, tech companies, or remote labeling teams
Industry UsageUsed in AI/ML research, developing annotation methodologiesUsed in preparing datasets for machine learning models
Search & Comparison IntentUnderstanding research-focused roles in data annotationLooking for practical data labeling jobs

Data Annotation Research involves exploring new annotation techniques and improving data quality for AI models, often requiring research skills. In contrast, Data Labeling Specialists focus on applying existing labeling tools to annotate datasets efficiently. Both roles are essential in AI development but differ in scope and expertise.

What are popular job titles related to Data Annotation Research jobs in California?

For Data Annotation Research jobs in California, the most frequently searched job titles are:

What job categories do people searching Data Annotation Research jobs in California look for?

The top searched job categories for Data Annotation Research jobs in California are:

What cities in California are hiring for Data Annotation Research jobs?

Cities in California with the most Data Annotation Research job openings:

Data Annotation Research jobs near you
Infographic showing various Data Annotation Research job openings in California as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 13% Part Time, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

AI Robotics Research Scientist

Nimble

San Francisco, CA • On-site

Full-time

Re-posted 24 days ago

Job description

Job Summary:
Nimble is an AI robotics company focused on building an autonomous supply chain to enhance commerce efficiency. The AI Robotics Research Scientist will design, develop, and implement robotic foundation models and reinforcement learning algorithms to automate tasks across the supply chain.
Responsibilities:
• Develop and train VLAs and generalist visuomotor policies to automate thousands of tasks across the supply chain
• Develop data collection pipelines
• Design data annotation and labeling
• Support implementation of models into production systems
Qualifications:
Required:
• P.h.D in Robotics or Computer Science
• Experience training deep learning models for robotic manipulation or mobility
• Experience working with real robotic hardware
• Experience training models in simulation and developing simulation environments
• Experience collecting custom datasets
• Experience training on open-source datasets
• Strong track record of publishing papers and/or deploying real-world applications
• Experience with system architecture, design and development
• Must be able to work extended hours and weekends as needed
• Must be able to work in San Francisco
Company:
Nimble is building the autonomous supply chain powered by generalist superhumanoid robots. Founded in 2017, the company is headquartered in San Francisco, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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