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Image Labeling Remote Jobs in San Ramon, CA (NOW HIRING)

Planet

Visiting Staff Scientist

San Francisco, CA · On-site +1

Deep Domain Expertise: 12+ years of experience in remote sensing and satellite image analysis, with ... Experience building automated pipelines for preprocessing and labeling planetary-scale datasets.

All JobsImage Labeling Remote JobsImage Labeling Remote Jobs in San Ramon, CA

Image Labeling Remote information

See San Ramon, CA salary details

$11

$15

$19

How much do image labeling remote jobs pay per hour?

As of Jul 4, 2026, the average hourly pay for image labeling remote in San Ramon, CA is $15.61, according to ZipRecruiter salary data. Most workers in this role earn between $13.99 and $17.21 per hour, depending on experience, location, and employer.

What is image labeling in a remote job?

Image labeling in a remote job involves tagging or annotating images with relevant information or categories from your home or any location outside of a traditional office. This process helps train machine learning models to recognize objects, people, or scenes within images. Remote image labelers use specialized software to identify and mark features according to project guidelines. The work is often flexible and may be paid per task or per hour, depending on the employer.

How much do AI labelers make?

AI labelers, including those working remotely in image labeling roles, typically earn between $10 and $20 per hour, depending on experience and the company. Many remote positions offer flexible schedules and may pay per task or image labeled rather than hourly.

What is the salary of image Labelling job?

The salary for an image labeling remote job typically ranges from $10 to $20 per hour, depending on experience, the company, and the complexity of the labeling tasks. Many positions are paid hourly or per task, and some may offer bonuses for accuracy or speed.

How can I make 2000 a week working from home?

In remote image labeling jobs, earning $2000 weekly typically requires working full-time hours, often around 40 hours per week, and completing high volumes of labeled images with accuracy. Success depends on experience, efficiency, and the pay rate per task, which varies by platform and project complexity. Building skills in image annotation tools and maintaining consistent productivity can help increase earnings to reach that goal.

What is the difference between Image Labeling Remote vs Data Annotation Specialist?

AspectImage Labeling RemoteData Annotation Specialist
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexible hoursRemote or on-site, flexible hours
Industry UsageAI, machine learning, computer visionAI, machine learning, data processing
Search & Comparison IntentOften compared for similar data labeling rolesRelated role in data preparation

Image Labeling Remote and Data Annotation Specialist roles both involve preparing data for AI systems, often working remotely with similar skills. However, Image Labeling Remote typically focuses specifically on labeling images and visual data, while Data Annotation Specialist may include a broader range of data types like text or audio. Both roles are essential in AI development and share similar work environments and skill requirements.

What are the key skills and qualifications needed to thrive as an Image Labeling Remote worker, and why are they important?

To thrive as an Image Labeling Remote worker, you need strong attention to detail, basic computer literacy, and familiarity with data annotation concepts, often supported by a high school diploma or equivalent. Proficiency with image labeling platforms such as Labelbox, Supervisely, or proprietary annotation tools is typically required. Reliability, self-motivation, and the ability to follow precise instructions make someone stand out in this position. These skills ensure that labeled data is accurate and consistent, which is crucial for training high-quality machine learning models.

What are some common challenges faced by remote image labeling professionals, and how can they be managed?

Remote image labeling professionals often encounter challenges such as maintaining focus during repetitive tasks, ensuring high accuracy, and communicating effectively with team members across different time zones. To manage these, it's helpful to set up a dedicated, distraction-free workspace, take regular breaks to prevent fatigue, and use collaboration tools like Slack or project management platforms to stay connected with the team. Adhering closely to labeling guidelines and participating in regular quality reviews also help maintain accuracy and consistency.

How to make $1000 a week remote?

To make $1000 a week as an image labeler remotely, you need to complete a high volume of accurate labeling tasks, often working for multiple platforms or clients simultaneously. Building experience, improving efficiency, and using tools like labeling software can help increase earnings, but consistent high-quality work is essential to reach that income level.
What job categories do people searching Image Labeling Remote jobs in San Ramon, CA look for? The top searched job categories for Image Labeling Remote jobs in San Ramon, CA are:
What cities near San Ramon, CA are hiring for Image Labeling Remote jobs? Cities near San Ramon, CA with the most Image Labeling Remote job openings:
Image Labeling Remote jobs near you
Visiting Staff Scientist

Visiting Staff Scientist

Planet

San Francisco, CA • On-site, Remote

Other

Medical, Dental, Vision, PTO

Posted 23 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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