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Data Annotation Research Jobs in Novato, CA (NOW HIRING)

Position Summary The Buck Institute for Research on Aging is seeking an exceptional, highly ... Developing systems that assist with literature mining, data annotation, hypothesis generation, and ...

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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 cities near Novato, CA are hiring for Data Annotation Research jobs?

Cities near Novato, CA with the most Data Annotation Research job openings:

AI Robotics Research Engineer

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. The AI Robotics Research Engineer will design, develop, train, and implement robotic foundation models and reinforcement learning algorithms for multi-agent robot fleets.
Responsibilities:
• Develop and train diffusion policies, VLMs, multi-agent deep RL policies and more
• Develop data collection pipelines
• Design data annotation and labeling
• Support implementation of models into production systems
Qualifications:
Required:
• Masters or 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.