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Hourly Ai Data Annotation Jobs in Riverside, CA (NOW HIRING)

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

Work closely with the labeling and data operations teams to define robust data annotation ... Our teams span AI, software, robotics engineering, product, field deployment, and technical ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

Work closely with the labeling and data operations teams to define robust data annotation ... Our teams span AI, software, robotics engineering, product, field deployment, and technical ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

Work closely with the labeling and data operations teams to define robust data annotation ... Our teams span AI, software, robotics engineering, product, field deployment, and technical ...

DataAnnotation

Treasury Analyst - AI Trainer

Fontana, CA · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DataAnnotation

Treasury Analyst - AI Trainer

Irvine, CA · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DataAnnotation

Financial Planner - AI Trainer

Hesperia, CA · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DataAnnotation

Treasury Analyst - AI Trainer

Riverside, CA · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DataAnnotation

Treasury Analyst - AI Trainer

Hesperia, CA · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DataAnnotation

Financial Planner - AI Trainer

Ontario, CA · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DataAnnotation

Financial Planner - AI Trainer

Menifee, CA · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DataAnnotation

Treasury Analyst - AI Trainer

Anaheim, CA · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DataAnnotation

Treasury Analyst - AI Trainer

Santa Ana, CA · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DataAnnotation

Financial Planner - AI Trainer

Irvine, CA · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

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All JobsHourly Ai Data Annotation JobsHourly Ai Data Annotation Jobs in Riverside, CA

Hourly Ai Data Annotation information

What is an hourly AI data annotation?

An Hourly AI Data Annotation job involves labeling, tagging, or categorizing data—such as images, text, or audio—to help train machine learning models. Annotators follow specific guidelines to ensure that the data is accurately labeled so that AI systems can learn to recognize patterns and make decisions. These jobs are typically paid by the hour and may require attention to detail, consistency, and sometimes familiarity with specialized annotation tools. This work is essential for improving the accuracy and usefulness of artificial intelligence applications.

What are some common challenges faced by hourly AI data annotators, and how can they be managed?

Hourly AI data annotators often encounter challenges such as repetitive tasks, maintaining high accuracy under time constraints, and adapting to evolving project guidelines. To manage these, it's important to take regular breaks to avoid fatigue, stay up to date with training materials, and communicate proactively with team leads if instructions are unclear. Many teams use collaborative tools and regular feedback sessions to support annotators and ensure consistent quality, making teamwork and attention to detail vital for success in this role.

What are the key skills and qualifications needed to thrive as an hourly AI data annotator?

To thrive as an AI Data Annotator, you need strong attention to detail, accuracy, and a basic understanding of data labeling concepts, typically supported by a high school diploma or equivalent. Familiarity with annotation tools such as Labelbox or Supervisely, and basic computer proficiency, are often required. Critical thinking, consistency, and effective communication are valuable soft skills in this role. These skills ensure high-quality, reliable data that directly improves the performance of AI and machine learning models.

What is the difference between Hourly Ai Data Annotation vs Data Labeler?

AspectHourly Ai Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or in-office, flexible hoursRemote or in-office, flexible hours
Industry UsageAI, machine learning, tech companiesAI, machine learning, tech companies
Job FocusAnnotating data for AI training, often with specific instructionsLabeling data to help AI models learn, often similar tasks

Hourly Ai Data Annotation and Data Labeler roles are similar, focusing on preparing data for AI systems. The main difference lies in terminology; 'Hourly Ai Data Annotation' emphasizes the paid hourly aspect and the specific task of annotating data for AI training, while 'Data Labeler' is a broader term used interchangeably in the industry. Both roles require similar skills and are used in the same industry sectors.

What are the most commonly searched types of Ai Data Annotation jobs in Riverside, CA? The most popular types of Ai Data Annotation jobs in Riverside, CA are:
What are popular job titles related to Hourly Ai Data Annotation jobs in Riverside, CA? For Hourly Ai Data Annotation jobs in Riverside, CA, the most frequently searched job titles are:
What job categories do people searching Hourly Ai Data Annotation jobs in Riverside, CA look for? The top searched job categories for Hourly Ai Data Annotation jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Hourly Ai Data Annotation jobs? Cities near Riverside, CA with the most Hourly Ai Data Annotation job openings:
Hourly Ai Data Annotation jobs near you

2.53 3D Machine Learning Engineer

FieldAI

Irvine, CA • On-site

Full-time

Re-posted 24 days ago

Job description

Job Summary:
FieldAI is a company based in Irvine, California, specializing in embodied AI and robotics. They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D machine learning models for processing reality capture data, contributing to automated progress tracking and scene understanding in construction environments.
Responsibilities:
• Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
• Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
• Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
• Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
• Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.
Qualifications:
Required:
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.
• 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.
• Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
• Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
• Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
• Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
• Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
• Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.
Preferred:
• Experience working with BIM data, digital twins, or construction-related sensor data.
• Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.
• Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
• Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
• Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
• Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
• Experience building custom modules for SparseConvNet or 3D transformers.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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