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Data Labeler Remote Jobs in California (NOW HIRING)

MemorialCare

Registered Dietitian PD

Long Beach, CA · On-site +1

$42/hr

Identifies and labels specific nutrition diagnosis(es) derived from the assessment data.Prioritizes ... May provide on-call coverage, and/or perform work that requires remote access and/or telecommuting.

Ventura Foods

Sr. Manager, Product Management

Irvine, CA · Remote

$135K - $179K/yr

Remote Position Summary: Position is responsible for the proactive management of sales margin ... This includes leveraging both MicroStrategy data via our datawarehouse and ensuring we have ...

Ventura Foods

Sr. Manager, Product Management

Irvine, CA · Remote

$135K - $178K/yr

Remote Position Summary: Position is responsible for the proactive management of sales margin ... This includes leveraging both MicroStrategy data via our datawarehouse and ensuring we have ...

TEKsystems

Phonetician

Menlo Park, CA · On-site +1

$40 - $45/hr

Responsibilities Perform narrow and broad phonetic transcriptions of speech data using the ... labeling conventions Collaborate with engineers, researchers, and project managers to clarify ...

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Data Labeler Remote information

What does a remote data labeler do?

A remote data labeler is responsible for annotating or tagging data—such as images, videos, audio, or text—from a remote location, typically working from home. Their work helps train machine learning models by providing accurate, labeled datasets that algorithms use to learn and make predictions. Data labelers follow specific guidelines to ensure consistency and accuracy, and may use specialized software tools to complete their tasks. This role is essential in industries like artificial intelligence, self-driving cars, and natural language processing. Remote data labelers often work as freelancers or as part of distributed teams for tech companies.

What are the key skills and qualifications needed to thrive as a remote data labeler?

To thrive as a Data Labeler Remote, you need strong attention to detail, basic data analysis skills, and familiarity with data annotation processes, often supported by a high school diploma or equivalent. Proficiency with labeling platforms, annotation tools, and sometimes knowledge of spreadsheet software are typically required. Reliability, time management, and effective communication are crucial soft skills for maintaining accuracy and meeting project deadlines in a remote setting. These skills ensure high-quality, consistent labeled data, which is essential for training reliable machine learning models.

What are some common challenges faced by remote data labelers and how can they be managed?

Remote data labelers often encounter challenges such as maintaining focus during repetitive tasks, ensuring consistent annotation quality, and communicating effectively with distributed teams. To manage these, it's helpful to establish a structured work routine, take regular breaks to prevent fatigue, and use annotation guidelines provided by employers. Leveraging collaboration tools for feedback and clarification also helps maintain high-quality output and fosters a sense of connection with team members.

What is the difference between Data Labeler Remote vs Data Annotator Remote?

AspectData Labeler RemoteData Annotator Remote
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexible hoursRemote, flexible hours
Industry UsageCommon in AI/ML data preparationCommon in AI/ML data preparation
Job FocusLabeling data points for machine learningAnnotating data for training AI models

Both Data Labeler Remote and Data Annotator Remote roles involve preparing data for AI and machine learning projects. While the terms are often used interchangeably, Data Labeler Remote typically emphasizes labeling data points, whereas Data Annotator Remote may include more detailed annotation tasks. Both roles require similar skills and are performed remotely, making them accessible for individuals seeking flexible data-related jobs.

How much does a data labeler remote make?

Remote data labelers typically earn between $12 and $20 per hour, depending on experience, complexity of tasks, and the company. Some positions may offer additional incentives or flexible schedules, but wages generally align with entry-level data annotation roles in the industry.

Is data labeling a good career?

Data labeling is a common entry-level role in the AI and machine learning industry, involving annotating data such as images, text, or audio to train algorithms. It often offers flexible remote work options and requires attention to detail but typically has lower barriers to entry and limited career advancement without additional skills or certifications.

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

The most popular types of Data Labeler jobs in California are:

What are popular job titles related to Data Labeler Remote jobs in California?

For Data Labeler Remote jobs in California, the most frequently searched job titles are:

What job categories do people searching Data Labeler Remote jobs in California look for?

The top searched job categories for Data Labeler Remote jobs in California are:

What cities in California are hiring for Data Labeler Remote jobs?

Cities in California with the most Data Labeler Remote job openings:

Data Labeler Remote jobs near you
Infographic showing various Data Labeler Remote job openings in California as of September 2026, with employment types broken down into 88% Full Time, 6% Part Time, and 6% Contract. Highlights an 100% Remote job distribution.

Sr Software Engineer, MLE - AV Labs

Sunnyvale, CA • On-site, Remote

Uber Technologies, Inc.
Ground Public Transportation • 10K+ employees
Learn more about Uber Technologies, Inc.external

Full-time

Retirement

Re-posted 7 days ago

Uber rating

6.8

Company rating: 6.8 out of 10

Based on 119 frontline employees who took The Breakroom Quiz

Job description

About the Role

Uber is launching AV Labs to accelerate the autonomous technology ecosystem. We're building out a high-velocity team of multi-disciplinary experts to turn real-world operations into high-quality data for our autonomous partners. This team is focused on the hardest problem in AV today: unlocking real-world, long-tail driving data. Autonomy is now a data race-and Uber has an edge: We collect rare, real-world driving data at a scale and capital efficiency no one else can match.

As a Senior ML Engineer, you will be at the forefront of Physical AI, building advanced autonomy algorithms and models to add rich semantics to our massive driving data. You will be responsible for the development and implementation of the latest machine learning techniques that enables better data mining, deep scene understanding, and causal modeling of ego vehicle behavior. The ideal candidate will be able to identify complex edge cases, provide robust algorithmic solutions, and set a high technical excellence bar.

What the Candidate Will Do

  • Algorithm Development: Lead the development of autonomy algorithms and foundation models that extract high-fidelity semantic meaning from complex urban edge cases to enrich our L4 data lake.
  • Systems Architecture Design: Architect scalable ML systems, including management of upstream sensor dependencies.
  • Technical Leadership: Partner with fellow engineers to architect, design, and build scalable solutions for ML technology that can stand the test of scale and availability.
  • Dataset Optimization: Deliver high-quality datasets to accelerate ML technologies through advanced sensor data collection, processing, and auto-labeling.
  • Cross-Functional Collaboration: Partner with platform, product, and security engineering teams to enable the successful deployment of the latest machine learning techniques into production.

Basic Qualifications

  • 4+ years of working experience in the ML/Robotics industry.
  • Bachelor's degree in Computer Science, Computer Engineering, or related fields.
  • Proficient in Python and Linux environments.
  • Familiar with modern AI/ML frameworks (e.g., PyTorch).

Preferred Qualifications

  • Experience in the Autonomous Driving domain.
  • Proven track record of deploying ML models in safety-critical physical systems.
  • Master's or PhD degree in Computer Vision, Robotics, or Machine Learning.
  • Familiarity with C++ and high-performance computing.

Ready to Ride?


This isn't the kind of place where you follow a playbook - it's where you help write one. If you're driven by impact, energized by challenge, and ready to shape how the world moves - we'd love to hear from you.


You may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits.


Offices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence. Ask your Recruiter for details about this role's requirements.


Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.

For Sunnyvale, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.

You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.

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