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Full Time Ai Data Annotation Jobs in California (NOW HIRING)

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

... data workflows, including collection, preprocessing, annotation, versioning, and model integration. • Implement and refine training strategies for large-scale AI systems, including vision, video ...

Abaka AI

Research Program Associate

Mountain View, CA · On-site

Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

Buck Institute

AI Data Scientist-Furman lab

Novato, CA · On-site

Developing systems that assist with literature mining, data annotation, hypothesis generation, and ... Salary range: $60,000-$75,000 , commensurate with experience * Full-time position * Exciting ...

TSMC

Senior Data Engineer / Data Curator

San Jose, CA · On-site

$124K - $168K/yr

As a Senior Data Engineer in the AI Data Curation track, you will design and maintain scalable data ... annotation tools and platforms for manual or semi-automated labeling. • Experience with NLP data ...

Turing

Client Director, Frontier Data - US

Palo Alto, CA · On-site

$255K - $325K/yr

Experience in AI data annotation, model evaluation, and fine-tuning platforms * Strong communication and storytelling skills with executive stakeholders Location SF Bay Area (Hybrid) Compensation ...

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Full Time Ai Data Annotation information

What is a full time AI data annotation job?

Full Time AI Data Annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Annotators play a crucial role in ensuring AI systems understand and process information accurately by providing high-quality, human-curated data. These positions usually require attention to detail, basic computer skills, and the ability to follow specific guidelines for different projects. Full-time roles typically offer stable hours and may be remote or on-site, depending on the employer.

What are the key skills and qualifications needed to thrive as a full time AI data annotation specialist?

To thrive as a Full Time AI Data Annotation Specialist, you need strong attention to detail, basic data literacy, and often a high school diploma or equivalent. Familiarity with annotation platforms (like Labelbox or Supervisely) and understanding of data labeling guidelines are typically required. Patience, consistency, and effective communication are soft skills that help ensure accuracy and clarity in collaborative projects. These skills and qualities are crucial for producing high-quality labeled data, which directly impacts the performance of AI models.

What are some common challenges faced by full time AI data annotation professionals, and how can they be addressed?

AI Data Annotation professionals often encounter challenges such as maintaining high accuracy while working with large datasets, interpreting ambiguous data, and consistently following complex labeling guidelines. These challenges can be addressed through thorough training, frequent communication with project managers or data scientists, and utilizing annotation tools with built-in quality checks. Collaboration with team members and regular feedback sessions also help ensure consistency and improve overall data quality, making the annotation process smoother and more efficient.

What is the difference between Full Time Ai Data Annotation vs Data Labeler?

AspectFull Time Ai Data AnnotationData Labeler
CredentialsHigh school diploma or equivalent; some roles prefer basic technical skillsHigh school diploma or equivalent; minimal technical requirements
Work EnvironmentOffice or remote; part of AI development teamsOffice or remote; often task-based or freelance
Industry UsageUsed across AI, machine learning, and data science industriesPrimarily in AI and machine learning industries for data preparation
Job ScopeFull-time, with responsibilities including data annotation, quality control, and collaborationTask-specific, focusing on labeling data accurately for AI training

Full Time Ai Data Annotation roles typically require more consistent hours, team collaboration, and a broader scope of responsibilities compared to Data Labelers, who often work on individual tasks with minimal oversight. Both roles are essential in AI development, but Full Time Ai Data Annotation offers more stability and integration within AI projects.

What are the most commonly searched types of Ai Data Annotation jobs in California?

The most popular types of Ai Data Annotation jobs in California are:

What are popular job titles related to Full Time Ai Data Annotation jobs in California?

For Full Time Ai Data Annotation jobs in California, the most frequently searched job titles are:

What job categories do people searching Full Time Ai Data Annotation jobs in California look for?

The top searched job categories for Full Time Ai Data Annotation jobs in California are:

What cities in California are hiring for Full Time Ai Data Annotation jobs?

Cities in California with the most Full Time Ai Data Annotation job openings:

Full Time Ai Data Annotation jobs near you
Infographic showing various Full Time Ai Data Annotation 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.

Machine Learning Engineer

Abaka AI

Mountain View, CA • On-site

Full-time

Re-posted 23 days ago

Job description

Job Summary:
Abaka AI is built on a mission to be the world’s most trusted data partner for AI companies. They are seeking a Machine Learning Engineer to design and develop scalable training pipelines for multimodal AI systems, collaborating with data engineering and research teams to drive the technical roadmap for model development.
Responsibilities:
• Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data.
• Work closely with data engineering and research teams to develop efficient data workflows, including collection, preprocessing, annotation, versioning, and model integration.
• Implement and refine training strategies for large-scale AI systems, including vision, video, and diffusion models, ensuring reproducibility, efficiency, and strong model performance.
• Develop tools and automation frameworks that accelerate model experimentation, hyperparameter tuning, and deployment.
• Identify and address performance bottlenecks in data or training pipelines to improve throughput, stability, and resource utilization.
• Collaborate with product and infrastructure teams to ensure smooth integration of model outputs into both internal and client-facing applications.
• Support internal best practices for model governance, experiment tracking, and documentation to maintain high engineering standards and reproducibility.
Qualifications:
Required:
• Strong academic background in computer science, artificial intelligence, machine learning, or related fields.
• 3+ years of experience in applied machine learning or ML engineering, with a demonstrated ability to deliver production-ready models or pipelines.
• Proficient in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, with hands-on experience in large-scale distributed training and inference systems.
• Familiarity with multimodal data processing (e.g., text-image pairing, video understanding, speech-audio modeling) and dataset optimization for model training.
• Solid understanding of ML system design, including feature pipelines, data loaders, model serving, and evaluation frameworks.
• Experience with modern infrastructure tools such as Kubernetes, Ray, Airflow, or MLflow, along with cloud-based training environments (AWS, GCP, Azure).
• Excellent communication and collaboration skills, capable of working effectively across engineering, research, and product teams to accomplish shared goals.
• Self-driven and adaptable, comfortable operating in a fast-paced startup environment, and able to demonstrate strong ownership and urgency in execution.
Preferred:
• Master’s degree or Ph.D. is preferred.
Company:
Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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