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Virtual Data Labelling Jobs in California (NOW HIRING)

Meta

Director, Data Licensing Partnerships

Menlo Park, CA · On-site

$296K - $356K/yr

... data labeling partners. • Lead complex commercial and technical negotiations, structuring ... Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual ...

QuinStreet

Data Analyst

Foster City, CA · Remote

$85K - $100K/yr

We run these virtual- and private-label marketplaces in one of the nation's largest media networks ... You will be required to develop a deep understanding of the related data sources and leverage them ...

QuinStreet

Senior Data Analyst

Foster City, CA · Remote

$80K - $130K/yr

We run these virtual- and private-label marketplaces in one of the nation's largest media networks ... You will be required to develop a deep understanding of the related data sources and leverage them ...

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Virtual Data Labelling information

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.

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 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.

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.

What are the most commonly searched types of Data Labelling jobs in California? The most popular types of Data Labelling jobs in California are:
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Virtual Data Labelling jobs near you
Senior Machine Learning Engineer, GenAI Data

Senior Machine Learning Engineer, GenAI Data

Roblox

San Mateo, CA

$139.50K - $183.90K/yr

Other

Posted 9 days ago

Job description

As a Senior Software Engineer on the Foundation AI organization, you will sit at the epicenter of our foundation model efforts. While the research world is focused on architecture, you will be the architect of the data flywheel that makes VideoGen and 3DGen possible. You aren't just building pipelines; you are building the infrastructure that defines how our models perceive and generate virtual worlds in three dimensions and across time.

In this role, you will partner directly with our AI researchers to advance beyond experimental datasets and into the realm of dynamic, high-fidelity data synthesis and evaluation. You will bridge the gap between research prototypes working locally to scaling for millions of users. You will design, implement, and scale robust, high-performance infrastructure to crawl, create, curate, store, and serve the massive datasets required for these models. We are seeking accomplished software engineers with a passion for data, experience building large distributed systems, and a commitment to writing high-quality, well-tested code to solve complex data challenges at scale. Your contributions will ensure that our foundation models receive the highest quality data, thereby supporting the next generation of creative AI.

You will:
  1. High-Scale Data Orchestration: Architect and maintain automated pipelines for the ingestion, cleaning, and pre-processing of multi-modal datasets (video, 3D,) spanning petabytes of data
  2. Synthetic Data Generation: Leverage image and video generation models to scale multi-modal synthetic datasets
  3. Research-to-Production Bridge: Partner with research teams to create training data for research experiments - research and implement synthetic data creation pipelines
  4. Scalable Evaluation Frameworks: Build and own evaluation-automating both heuristic-based metrics and human-in-the-loop interfaces to evaluate and benchmark training datasets and in-house foundation models
  5. Model Deployment & API Architecture: Design and optimize high-throughput, low-latency Inference APIs for internal and external consumer access
  6. Autonomous SOTA Tracking: Actively participate in literature reviews and paper reading groups to identify and implement the latest optimizations in generative modeling
  7. Resource Efficiency & Observability: Implement monitoring pipeline health, optimizing data loading to ensure GPUs are used efficiently
You have:
  • 8+ years of experience as a research-focused data systems engineer (preferably working with 3D and video foundation models)
  • Expertise in building scalable ML data pipelines for both batch and real-time environments. Experience working with and processing very large datasets (Petabytes or more).
  • Versatile: You're a generalist and you are comfortable with several languages and technologies already; you are adaptable in any situation
  • Team-Player & Technical Leader: You are a collaborative team member who actively mentors peers, drives technical excellence, and takes ownership of leading and delivering key features and projects across team boundaries
  • Python Proficiency: You can write high-quality Python code for automation, tooling, and infrastructure management
  • Experience with cloud data platforms and distributed processing technologies (e.g., Spark, Ray, Kubeflow, S3, etc.).
  • Are passionate about the potential of generative AI, particularly in creative domains like 3D/4D content.
  • A Bachelor's degree or equivalent experience in Computer Science, Computer Engineering, or a similar technical field
You are: 
  • MLOps Experience: Knowledge of experiment tracking (Weights & Biases, MLflow) and versioning for massive datasets.
  • Custom Tooling Development: Experience building internal "human-in-the-loop" tools for data labeling specific to video or 3D.
  • C++ Knowledge: Optimize the performance of data loaders and being comfortable modifying engine code.
  • Game development and digital content creation tools: Experience with making Roblox games, using Blender, Unreal Engine, or Unity.
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