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Remote Data Annotation Jobs in Berkeley, CA (NOW HIRING)

Planet

Visiting Staff Scientist

San Francisco, CA · On-site +1

You will collaborate with a multi-disciplinary team of "Planeteers" across space operations, data ... Deep Domain Expertise: 12+ years of experience in remote sensing and satellite image analysis, with ...

Troveo AI Inc

Technical Product Manager

San Francisco, CA · On-site +1

$150K - $200K/yr

... data pipeline covering profiling, deduplication, transcoding, annotation, and downstream delivery ... Unlimited PTO with encouragement to actually use it * Remote First Policy : Work from anywhere in ...

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Remote Data Annotation information

See Berkeley, CA salary details

$11

$38

$85

How much do remote data annotation jobs pay per hour?

As of Jul 5, 2026, the average hourly pay for remote data annotation in Berkeley, CA is $38.27, according to ZipRecruiter salary data. Most workers in this role earn between $19.60 and $49.67 per hour, depending on experience, location, and employer.

How hard is it to get hired by data annotation?

Getting hired for a remote data annotation role typically requires basic computer skills, attention to detail, and sometimes familiarity with annotation tools or platforms. Many positions are entry-level and do not require advanced education, making the application process relatively accessible, though competition can vary based on the employer and job volume.

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

To thrive as a Remote Data Annotation specialist, strong attention to detail, accuracy, and familiarity with basic data processing concepts are essential, often requiring a high school diploma or equivalent. Experience using data labeling platforms, annotation tools (such as Labelbox or Supervisely), and sometimes familiarity with spreadsheet software may be required. Excellent time management, communication skills, and the ability to work independently are valuable soft skills in this remote role. These skills are vital to ensure that data annotations are consistent, precise, and delivered on schedule, which directly impacts the quality of AI and machine learning outcomes.

How to make $1000 a week remote?

Remote data annotation jobs typically pay per task or hour, with earnings varying based on experience, accuracy, and the complexity of the data. To make $1000 a week, you need to work consistently, often requiring 20-40 hours depending on pay rates, which can range from a few cents to several dollars per annotation. Building skills in specific tools and maintaining high accuracy can help increase your earning potential in this field.

What are the typical daily tasks for someone working in Remote Data Annotation?

Daily tasks for a Remote Data Annotation role usually involve reviewing and labeling large volumes of data—such as images, audio clips, text, or video—according to specific project guidelines. You will use specialized annotation tools to identify objects, transcribe content, categorize information, or tag relevant features to support machine learning projects. Communication with project managers or quality assurance teams may be necessary for feedback and clarity on guidelines. Most roles also require regular self-checks for accuracy and the ability to meet productivity quotas or deadlines. This structure allows for a combination of focused individual work and occasional team collaboration to ensure project goals are met.

How can I make 2000 a week working from home?

Remote data annotation jobs can pay between $10 and $20 per hour, so earning $2000 weekly would require working approximately 100 to 200 hours. Increasing income may involve taking on multiple projects, improving accuracy to access higher-paying tasks, or gaining specialized skills in areas like medical or AI data annotation. Consistent work and efficient time management are essential to reach this income level.

What is a Remote Data Annotation job?

A Remote Data Annotation job involves labeling, tagging, or categorizing data (such as images, text, audio, or video) to help improve machine learning models. This work is typically done from home using specialized annotation tools provided by employers. Accuracy and attention to detail are essential, as the quality of annotations directly impacts AI model performance. Many companies hire remote annotators on a freelance, part-time, or contractual basis.

Does data annotation actually pay?

Data annotation jobs typically pay hourly or per task rates, with compensation varying based on complexity and platform. Many remote data annotation roles offer competitive pay, especially for experienced annotators using tools like labeling software, and some positions provide consistent income. However, pay rates can differ widely across companies and projects, so it is important to research specific opportunities.
What are the most commonly searched types of Data Annotation jobs in Berkeley, CA? The most popular types of Data Annotation jobs in Berkeley, CA are:
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Remote Data Annotation jobs near you
Infographic showing various Remote Data Annotation job openings in Berkeley, CA as of June 2026, with employment types broken down into 73% Full Time, 22% Part Time, 4% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $79,595 per year, or $38.3 per hour.
Visiting Staff Scientist

Visiting Staff Scientist

Planet

San Francisco, CA • On-site, Remote

Other

Medical, Dental, Vision, PTO

Posted 24 days ago

Job description

About the Role:

We are seeking a distinguished Visiting Staff Scientist to join our AI Research (AIR) team for a one-year sabbatical residency. In this role, you will play a pivotal part in our mission to create a "Queryable Earth" by leading the development of Planet's proprietary geospatial foundation models (GFMs).

While Planet has historically leveraged external models like Google's RSFM and RemoteCLIP, we are now focused on building in-house models specifically trained on our unique imagery. You will lead research into creating temporally dense embeddings that go beyond static annual summaries, capturing the dynamic and ephemeral nature of our planet-from rapid flooding to disaster impacts.

You will collaborate with a multi-disciplinary team of "Planeteers" across space operations, data pipelines, and analytics to co-develop AI/ML solutions that leverage the high spatial resolution and near-daily revisit of PlanetScope data.

Impact You'll Own:

  • Develop Planet's Proprietary GFM: Lead the research and development of a foundation model specifically trained on Planet imagery, incorporating the time-axis to create high-cadence time-series embeddings.
  • Benchmark Geospatial Architectures: Systematically evaluate and compare existing GFMs (e.g., TerraMind, Prithvi, Clay) against PlanetScope data to assess performance, computational cost, and transferability.
  • Capture Dynamic Earth Events: Design embeddings and workflows optimized for detecting short-lived, high-impact events such as floods, rapid surface-water expansion, and fire.
  • Multi-Sensor Integration: Explore the synergy between PlanetScope, Sentinel-1 SAR, and other commercial SAR data to ensure robust time-series analysis even under cloud cover.
  • Human-in-the-Loop Innovation: Use embeddings to design active learning workflows that prioritize labeling and reduce the annotation burden for time-sensitive mapping tasks.
  • Academic & Technical Leadership: Publish findings in top-tier journals and present at conferences (e.g., IGARSS, CVPR), highlighting PlanetScope's unique value in the foundation model ecosystem.
  • Mentor & Collaborate: Oversee the technical direction of a dedicated postdoc and collaborate with Planet's research scientists to transition prototypes into operational products.

What You Bring:

  • Distinguished Academic Background: PhD and current Faculty/Professor status in Geospatial Analytics, Computer Science, Remote Sensing, or a related field.
  • Deep Domain Expertise: 12+ years of experience in remote sensing and satellite image analysis, with a proven track record in building AI-based models for environmental change (e.g., flood-extent, water dynamics).
  • Multimodal AI Fluency: Extensive experience with foundation models, contrastive learning (CLIP-like models), and multi-model vision-language models (MMVLMs).
  • Advanced Geospatial Toolkit: Proficiency in multi-sensor integration (Landsat, Sentinel-2, PlanetScope, Sentinel-1) and high-resolution mapping at varying scales (3m, 10m, 30m).
  • Technical Proficiency: Expert-level Python skills and experience with the scientific stack (xarray, Dask, NumPy, Rasterio, GeoPandas) and deep learning frameworks.
  • Scale-Minded Research: Experience building automated pipelines for preprocessing and labeling planetary-scale datasets.
  • Collaborative Spirit: A history of leading research labs and a desire to work in a fast-paced, industrial R&D environment.

What Makes You Stand Out:

  • Specialized Environmental Research: Extensive experience specifically in flood damage quantification and methane-related water dynamics.
  • Proven Funding & Publication Record: History of leading NASA-funded or similar high-impact geospatial research projects.
  • Architectural Knowledge: Direct experience fine-tuning or modifying specific GFM architectures like TerraMind or Prithvi.

Hybrid Experience: A mix of deep academic rigor and the ability to prototype rapid-change monitoring tools for operational readiness.

Application Deadline:

August 11, 2026 by 11:59p / 23:59 CET (Central European Time)

Benefits While Working at Planet:

These offerings are dependent on employment type and geographical location, based upon applicable law or company policy.

  • Comprehensive Medical, Dental, and Vision plans
  • Health Savings Account (HSA) with a company contribution
  • Generous Paid Time Off in addition to holidays and company-wide days off 
  • 16 Weeks of Paid Parental Leave
  • Wellness Program and Employee Assistance Program (EAP)
  • Home Office Reimbursement
  • Monthly Phone and Internet Reimbursement
  • Tuition Reimbursement and access to LinkedIn Learning
  • Equity
  • Commuter Benefits (if local to an office)
  • Volunteering Paid Time Off

Compensation:

The US base salary range for this full-time position at the commencement of employment is listed below. Additionally, this role might be eligible for discretionary short-term and long-term incentives (bonus and equity). The final salary range is determined by job related experience, skills and location. The range displays our typical hiring range for new hire salaries in US locations only. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

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