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Remote Data Labeling Jobs in California (NOW HIRING)

Career Renew

Applied ML Engineer

San Francisco, CA · On-site +1

... remote in Boston, SF Bay Area, San Diego or Los Angeles. This role will require a security ... Contribute to dataset development and labeling strategy, including data augmentation, synthetic ...

Planet

Visiting Staff Scientist

San Francisco, CA · On-site +1

You will collaborate with a multi-disciplinary team of "Planeteers" across space operations, data ... Use embeddings to design active learning workflows that prioritize labeling and reduce the ...

Mercor

Information Security Analyst

San Francisco, CA · Remote

$1K - $2K/wk

Remote Role Responsibilities * Review and evaluate AI-generated outputs related to threat analysis ... Annotate, label, and validate data across cybersecurity use cases like CVE classification accuracy ...

New

Mercor

Telecom Network Expert - AI

San Francisco, CA · Remote

$1K - $1K/wk

Remote Role Responsibilities * Review and evaluate AI-generated outputs related to network ... Annotate, label, and validate data across telecom use cases, ensuring accuracy in network topology ...

New

Mercor

Security Analyst

San Francisco, CA · Remote

$1K - $2K/wk

Remote Role Responsibilities * Review and evaluate AI-generated outputs related to threat analysis ... Annotate, label, and validate data across cybersecurity use cases like CVE classification accuracy ...

Mercor

Senior Security Analyst

San Francisco, CA · Remote

$1K - $2K/wk

Remote Role Responsibilities * Review and evaluate AI-generated outputs related to threat analysis ... Annotate, label, and validate data across cybersecurity use cases like CVE classification accuracy ...

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How much do remote data labeling jobs pay per hour?

As of Jun 6, 2026, the average hourly pay for remote data labeling in California is $33.63, according to ZipRecruiter salary data. Most workers in this role earn between $16.71 and $45.43 per hour, depending on experience, location, and employer.

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

Remote data labelers often face challenges such as maintaining focus during repetitive tasks, managing volume-based workloads, and interpreting ambiguous data with consistency. To manage these, it's important to set up a distraction-free workspace, take regular breaks to avoid fatigue, and seek clarification from supervisors or project guidelines when uncertainties arise. Most companies provide onboarding and ongoing support to help new labelers understand annotation standards and best practices. Collaborating with remote team members via chat or project management platforms also helps maintain quality and stay connected. By being proactive and utilizing available resources, remote data labelers can maintain high accuracy and productivity.

What are the key skills and qualifications needed to thrive in the Remote Data Labeling position, and why are they important?

To thrive as a Remote Data Labeling specialist, you need strong attention to detail, basic data analysis skills, and the ability to accurately tag and categorize diverse data types, often with a high school diploma or equivalent. Familiarity with data labeling platforms, annotation tools (such as Labelbox or Amazon SageMaker Ground Truth), and, occasionally, basic knowledge of data privacy standards is helpful. Time management, self-discipline, and effective remote communication are valuable soft skills in this position. These skills ensure that labeled data is accurate and reliable, supporting the success of machine learning and AI projects.

What is a Remote Data Labeling job?

A Remote Data Labeling job involves annotating or categorizing data, such as images, text, audio, or video, to train machine learning models. Workers review and tag content based on specific guidelines provided by companies. This job is typically done online from home and requires attention to detail, consistency, and sometimes specialized domain knowledge. It plays a crucial role in improving artificial intelligence systems by providing high-quality labeled data.

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

Applied ML Engineer

Career Renew

San Francisco, CA • On-site, Remote

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Description Job Description Career Renew is recruiting for one of its clients an Applied ML Engineer - this is a hybrid role in Arlington, VA or remote in Boston, SF Bay Area, San Diego or Los Angeles. This role will require a security clearance. Salary range:190-250K USD yearly plus benefits plus equity.

We are seeking a versatile and pragmatic Applied ML Engineer to contribute across a broad range of machine learning and perception tasks that power our edge-intelligent maritime systems. This role requires someone comfortable wearing many hats—from working with computer vision and sensor fusion models to building lightweight inference pipelines, designing experiments, and fine-tuning model behavior in production. You'll work closely with a cross-functional team spanning hardware, software, and product to deliver real-world AI solutions that are robust, efficient, and reliable under challenging field conditions.

This is an ideal position for someone who thrives on variety, rapidly shifting problem domains, and turning rough ideas into deployed systems. Key Responsibilities: Design, train, and evaluate models for tasks ranging from object detection and classification to anomaly detection and sensor-based inference. Optimize model architectures and inference pipelines for performance on embedded/edge hardware under compute and bandwidth constraints.

Contribute to dataset development and labeling strategy, including data augmentation, synthetic data generation, and domain adaptation. Support prototyping and experimentation across a variety of AI subfields, including computer vision, signal processing, and multi-modal fusion. Implement real-time pipelines for processing sensor data on-device and in cloud environments.

Develop tools and scripts for benchmarking, data visualization, and debugging ML model performance. Stay current with the latest research and tools in machine learning and evaluate their applicability to our product roadmap. Participate in code reviews, team knowledge sharing, and internal technical documentation.

Must be eligible to obtain/maintain a security clearance. Qualifications (Preferred): Master's or PhD in Computer Vision, Machine Learning, Robotics, or related field. Bachelors candidates considered on a case by case basis.

4+ years of experience building and deploying machine learning models in production environments. Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow. Comfortable working with a range of data types (images, time-series, geospatial, RF, etc.).

Experience with edge or embedded ML deployments, including model compression and hardware-aware optimization. Familiarity with common ML practices including cross-validation, hyperparameter tuning, and model monitoring. Excellent debugging, experimentation, and problem-solving skills.

Strong collaboration and communication skills with both technical and non-technical team members. Bonus: experience in maritime, aerospace, or other remote sensing domains.

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