ZipRecruiter

Log In

1

Virtual Data Labelling Jobs in California (NOW HIRING)

Synchrony Financial

You will be required to commuteto your nearestHub (either virtual or physical) for in-person ... label credit * 2+ years of experience in Credit, Collections, Risk, Finance, or other Data ...

Aerotek

Shipping & Receiving Clerk

Huntington Beach, CA · On-site

$18 - $20/hr

Affix shipping labels to packed cartons or stencil identifying shipping information on cartons ... Strong data entry skills for accurately maintaining shipping, receiving, and inventory records in ...

Meta

Lead, Product Content Engineering

Menlo Park, CA

$193K/yr

They will be expected to perform data analytical work, comprehend complex recommendation systems ... Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual ...

next page

Showing results 1-20

All JobsVirtual Data Labelling JobsVirtual Data Labelling Jobs in California

Virtual Data Labelling information

What is the difference between Virtual Data Labelling vs Data Annotation Specialist?

AspectVirtual Data LabellingData Annotation Specialist
CredentialsBasic computer skills, training in labelling toolsSimilar, often requires training in annotation software
Work EnvironmentRemote, online platformsRemote or on-site, depending on employer
Industry UsageAI, machine learning, autonomous vehiclesAI, computer vision, NLP projects
Search IntentLabeling data for AI modelsAnnotating data for machine learning

Both roles involve preparing data for AI systems, but Virtual Data Labelling focuses on assigning labels to datasets using online tools, while Data Annotation Specialists may perform more detailed annotations, often requiring specific domain knowledge. Both are essential in AI development and share similar work environments and skill requirements.

How much do data labelers make?

Data labelers typically earn between $10 and $20 per hour, depending on experience, complexity of tasks, and the employer. Many roles are freelance or part-time, with some positions offering bonuses for accuracy or speed.

Is data labelling a good career?

Data labelling is a common entry-level role in data annotation and machine learning, offering opportunities to develop skills in data management and understanding AI workflows. It often requires attention to detail and familiarity with tools like annotation software, with flexible schedules and remote work options available. Career growth can lead to roles in data analysis, quality assurance, or AI development.

What is virtual data labelling?

Virtual data labelling is the process of annotating or tagging data, such as images, videos, or text, through online platforms to make it understandable for machine learning algorithms. Data labelers work remotely to identify and categorize objects, features, or information within datasets, which helps train artificial intelligence systems. This job is essential in industries like autonomous vehicles, healthcare, and e-commerce, where large volumes of labelled data are needed to improve AI accuracy.

How can I make $2000 a week working from home?

Virtual data labeling jobs can offer flexible income, but earning $2000 weekly typically requires completing a high volume of tasks or working multiple projects simultaneously. Success depends on experience, efficiency, and access to platforms that pay well, such as those offering premium or specialized labeling tasks.

How to make $1000 a week remote?

Virtual Data Labelling jobs can help you earn income remotely by labeling datasets for AI and machine learning projects. To make $1000 a week, you typically need to work consistently, complete high-volume labeling tasks efficiently, and possibly specialize in areas like image or audio annotation. Building a strong profile, gaining experience, and using platforms that pay well can increase your earning potential.

How does a virtual data labeller typically collaborate with data scientists and machine learning engineers?

Virtual data labellers play a crucial role in supporting data scientists and machine learning engineers by accurately tagging data that will be used to train and validate models. Collaboration often occurs through project management tools or direct communication platforms, where labellers receive guidelines and feedback to ensure consistency and quality. Regular check-ins or quality audits are common, and labellers may join virtual meetings to clarify requirements or discuss ambiguous cases. This teamwork helps ensure that the labelled data meets project standards and contributes to the success of AI initiatives.

What are the key skills and qualifications needed to thrive as a Virtual Data Labeller, and why are they important?

To thrive as a Virtual Data Labeller, you need strong attention to detail, accuracy, and basic data processing skills, typically supported by a high school diploma or relevant experience. Familiarity with data annotation tools, content management systems, and sometimes basic programming or spreadsheet software is important. Strong time management, focus, and effective communication skills help you meet deadlines and collaborate with remote teams. These abilities are crucial to ensure high-quality, consistent data labelling that directly impacts the performance of machine learning models.
What are the most commonly searched types of Data Labelling jobs in California? The most popular types of Data Labelling jobs in California are:
What are popular job titles related to Virtual Data Labelling jobs in California? For Virtual Data Labelling jobs in California, the most frequently searched job titles are:
What job categories do people searching Virtual Data Labelling jobs in California look for? The top searched job categories for Virtual Data Labelling jobs in California are:
What cities in California are hiring for Virtual Data Labelling jobs? Cities in California with the most Virtual Data Labelling job openings:
Virtual Data Labelling jobs near you

Isaac Sim Expert / Simulation Engineer

ConfigUSA

Mountain View, CA • On-site

Contractor

Posted 8 days ago

Job description

Simulation Engineer

Pre-Screening Questionnaire:

-Have you worked on an Isaac Sim project?

-Do you have exposure to simulation projects?

Must Have Technical/Functional Skills:

Proficiency in NVIDIA Isaac Sim, Omniverse, and synthetic data generation tools Strong understanding of CAD model handling and 3D asset integration Experience with Python, C++, and simulation scripting Familiarity with ROS/ROS2, Gazebo, Unity, or other simulation platforms is a plus Knowledge of computer vision and deep learning frameworks (PyTorch, TensorFlow, OpenCV) Prior experience in auto-labeling pipelines and dataset generation for AI models Publications, patents, or projects in simulation-based robotics development are a plus

Roles & Responsibilities:

Develop and maintain simulation environments using NVIDIA Isaac Sim and Omniverse Integrate CAD models and create synthetic datasets for training computer vision models Implement auto-annotation pipelines for object detection, segmentation, and tracking Simulate multi-sensor setups (camera, LiDAR, radar, depth sensors) for perception validation Support testing of vision-based algorithms in virtual environments before real-world deployment Collaborate with AI and robotics teams to simulate robotic tasks like pick-and-place, navigation, and human-robot interaction Optimize simulation performance and realism for accurate model training and testing

Generic Managerial Skills, If any:

The engineer will work closely with AI researchers and robotics engineers to simulate real-world scenarios and optimize perception systems..

Key Words to search in Resume:

Isaac Sim Expert, Simulation Engineer, Synthetic Data Generation, CAD Integration, Robotics Simulation, Omniverse, Auto-Annotation, Multi-Sensor Simulation, AI for Robotics

Education:

 Master’s/bachelor’s in computer science, AI, Robotics, or related field