ZipRecruiter

Log In

2

Entry Level Amazon Data Annotation Jobs (NOW HIRING)

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

Work closely with the labeling and data operations teams to define robust data annotation ... Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track ...

SynergisticIT

data (Entry Level)

San Francisco, CA · On-site

$20 - $26.75/hr

For Data Scientists/Machine learning roles: * Python * Django * Deep Learning * NLP Benefits of ... Our Candidates always get projects with well-known IT firms like Google, Apple, PayPal, Amazon, etc ...

SynergisticIT

Entry Level Data Scientiest

Los Angeles, CA · On-site

$18 - $24/hr

Hands on experience on platforms - Microsoft Azure/Amazon Web Services * Libraries - Numpy, Pands, Scikit learn, Seaborn, Plotly, TensorFLow, Should be able to do data visualisation using Tableau or ...

SynergisticIT

Entry Level Data Scientiest

Manchester, NH · On-site

$16.25 - $21.75/hr

Hands on experience on platforms - Microsoft Azure/Amazon Web Services * Libraries - Numpy, Pands, Scikit learn, Seaborn, Plotly, TensorFLow, Should be able to do data visualisation using Tableau or ...

next page

Showing results 1-20

All JobsAmazon Data Annotation JobsEntry Level Amazon Data Annotation Jobs

Entry Level Amazon Data Annotation information

See salary details

$11

$20

$31

How much do entry level amazon data annotation jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for entry level amazon data annotation in the United States is $20.24, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $21.88 per hour, depending on experience, location, and employer.

Will Amazon pay you $28 an hour to work from home?

Entry level Amazon data annotation jobs typically do not pay $28 an hour; wages are usually lower and depend on the specific task, experience, and company policies. These roles often involve labeling data for machine learning and may require basic computer skills and attention to detail. Pay rates vary and are generally less than $28 per hour for entry-level positions.

What are entry level Amazon data annotation jobs?

Entry level Amazon data annotation jobs involve labeling, tagging, categorizing, or otherwise annotating data such as images, text, or audio to help improve machine learning models and artificial intelligence systems. These roles typically require attention to detail, the ability to follow guidelines, and may involve repetitive tasks. No advanced technical skills are usually required, making them suitable for those new to the tech industry or individuals seeking remote and flexible work opportunities. Employees may work on platforms like Amazon Mechanical Turk or directly for Amazon’s internal data teams. This work is essential for ensuring the quality and accuracy of AI-driven products and services.

How much do Amazon data annotation jobs pay?

Amazon data annotation jobs typically pay between $10 and $15 per hour for entry-level positions. Compensation can vary based on location, experience, and the complexity of annotation tasks, which often involve using specialized tools and adhering to quality standards.

Is it easy to get hired for data annotation?

Entry level Amazon data annotation jobs are generally accessible to candidates with basic computer skills and attention to detail. The hiring process often involves simple assessments or training, making it relatively straightforward for those interested in starting a data annotation career.

What are the key skills and qualifications needed to thrive as an Entry Level Amazon Data Annotation Specialist, and why are they important?

To thrive as an Entry Level Amazon Data Annotation Specialist, you need attention to detail, basic computer literacy, and the ability to follow precise instructions, often supported by a high school diploma or equivalent. Familiarity with data labeling platforms, annotation tools, and common office software like Excel is typically required. Strong communication, time management, and the ability to work independently are valuable soft skills in this role. These skills ensure accurate data processing, contribute to high-quality machine learning outputs, and help maintain productivity in a deadline-driven environment.

What are some common challenges faced by entry-level Amazon data annotation specialists, and how can they be addressed?

Entry-level Amazon data annotation specialists often encounter challenges such as maintaining high accuracy while working with repetitive tasks, adapting to evolving project guidelines, and managing productivity targets. To address these, it's important to develop a strong attention to detail, seek clarification on ambiguous instructions, and utilize any available training or feedback sessions. Building a habit of double-checking your work and staying organized can also help improve both speed and quality, leading to better performance and growth opportunities within the team.

Can I do data annotation with no experience?

Entry level Amazon data annotation jobs typically do not require prior experience, as training is often provided to teach the necessary skills. Basic computer literacy and attention to detail are usually sufficient to start, and familiarity with annotation tools can be helpful. These roles are suitable for beginners looking to gain experience in data labeling and machine learning workflows.
More about Entry Level Amazon Data Annotation jobs
What cities are hiring for Entry Level Amazon Data Annotation jobs? Cities with the most Entry Level Amazon Data Annotation job openings:
What are the most commonly searched types of Amazon Data Annotation jobs? The most popular types of Amazon Data Annotation jobs are:
What job categories do people searching Entry Level Amazon Data Annotation jobs look for? The top searched job categories for Entry Level Amazon Data Annotation jobs are:
Entry Level Amazon Data Annotation jobs near you
Infographic showing various Entry Level Amazon Data Annotation job openings in the United States as of July 2026, with employment types broken down into 72% Full Time, 22% Part Time, and 6% Contract. Highlights an 100% In-person job distribution, with an average salary of $42,098 per year, or $20.2 per hour.

3D Machine Learning Engineer

FieldAI

Irvine, CA • On-site

Full-time

Re-posted 20 days ago

Job description

FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.
What You’ll Do
  • Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
  • Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
  • Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
  • Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
  • Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.
What You Have
  • Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.
  • 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.
  • Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
  • Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
  • Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
  • Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
  • Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
  • Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.
The Extras That Set You Apart
  • Experience working with BIM data, digital twins, or construction-related sensor data.
  • Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.
  • Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
  • Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
  • Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
  • Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
  • Experience building custom modules for SparseConvNet or 3D transformers.
Our salary range is generous and we consider each individual’s background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.

Why Join FieldAI in Irvine?
In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics’ hardest challenges: reliable deployment outside the lab. Our Field Foundational Models™ raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use.
You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments.

Be Part of the Next Robotics Revolution
We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world.

Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like.

We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected statu

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.