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Amazon Data Annotation Jobs in California (NOW HIRING)

Forward Deployed Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Advanced annotation tools, workflow automation, and quality control systems that enable teams to ... Google, Meta, Amazon. You will work with human data teams or AI researchers in customer ...

Senior Perception Engineer

San Francisco, CA

$123K - $169K/yr

... Tesla, and Amazon Robotics, Chef is rapidly scaling with multiple multi-year contracts and a ... Own the end-to-end ML lifecycle: data collection strategy, annotation tooling, dataset curation ...

... Tesla, and Amazon Robotics, Chef is rapidly scaling with multiple multi-year contracts and a ... Own the end-to-end ML lifecycle: data collection strategy, annotation tooling, dataset curation ...

... Tesla, and Amazon Robotics, Chef is rapidly scaling with multiple multi-year contracts and a ... Own the end-to-end ML lifecycle: data collection strategy, annotation tooling, dataset curation ...

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How much do amazon data annotation jobs pay per hour?

As of Jun 11, 2026, the average hourly pay for amazon data annotation in California is $23.59, according to ZipRecruiter salary data. Most workers in this role earn between $16.48 and $29.19 per hour, depending on experience, location, and employer.

How much do Amazon data annotation jobs pay?

Amazon data annotation jobs typically pay between $12 and $20 per hour, depending on experience, location, and the complexity of the tasks. These roles often require attention to detail and familiarity with annotation tools, and may be part-time or flexible schedules.

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

To thrive as an Amazon Data Annotation specialist, you need keen attention to detail, accuracy, and proficiency in data labeling or annotation, often supported by a background in data entry or related fields. Familiarity with annotation tools, Amazon’s proprietary data platforms, and in some cases basic understanding of programming languages or machine learning concepts is beneficial. Strong communication skills, adaptability, and the ability to work independently or with minimal supervision help individuals excel in the role. These abilities are crucial for ensuring high-quality, reliable data that supports Amazon’s AI and machine learning initiatives.

What is an Amazon Data Annotation job?

An Amazon Data Annotation job involves labeling or tagging data such as text, images, audio, or videos to improve machine learning models. Annotators follow specific guidelines to provide accurate labels that help refine Amazon's AI systems, including Alexa and product recommendations. This work is often detail-oriented and may require understanding context, language nuances, or specific industry knowledge. The role can be full-time or contract-based and may involve remote or on-site work, depending on the project.

What does a typical day look like for an Amazon Data Annotation specialist?

A typical day as an Amazon Data Annotation specialist involves reviewing, labeling, and annotating diverse datasets, such as images, videos, or text, using specialized software and following detailed guidelines. You may collaborate with team members or project leads to clarify instructions and ensure consistency across annotations. Periodic quality checks and feedback sessions are common, helping you refine your work and maintain high standards. While much of the work is independent, clear communication and responsiveness are important for meeting project deadlines and successfully supporting Amazon’s AI development goals.

Will Amazon pay me to work from home?

Amazon Data Annotation jobs are often remote positions that pay employees for work done from home. These roles typically involve using online tools and require good attention to detail, with pay rates varying by position and location. Candidates should review specific job postings for exact compensation details and work requirements.

How to get hired for data annotation?

To get hired for data annotation roles, candidates should have strong attention to detail, basic computer skills, and familiarity with annotation tools or platforms. Many employers look for prior experience or training in data labeling, and some positions may require completing a short assessment or test. Applying through online job boards and demonstrating accuracy and reliability can improve chances of hiring.

Does data annotation actually pay well?

Data annotation jobs, including roles like Amazon Data Annotation, typically offer hourly wages that are close to minimum wage or slightly above, depending on the company and location. While some positions may pay more for specialized skills or experience, overall pay tends to be modest and is often considered entry-level work. Compensation can vary based on the complexity of tasks and whether the role is freelance or full-time.
What are the most commonly searched types of Amazon Data Annotation jobs in California? The most popular types of Amazon Data Annotation jobs in California are:
What job categories do people searching Amazon Data Annotation jobs in California look for? The top searched job categories for Amazon Data Annotation jobs in California are:
What cities in California are hiring for Amazon Data Annotation jobs? Cities in California with the most Amazon Data Annotation job openings:
Infographic showing various Amazon Data Annotation job openings in California as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $49,077 per year, or $23.6 per hour.

AI/ML Engineer - Architectural Drawing Understanding (US)

Genia

Los Angeles, CA

Other

Posted 4 days ago


Job description

AI/ML Engineer – Architectural Drawing Understanding (US)

Los Angeles, CA, USA

Responsibilities

We are seeking an AI/ML Engineer with strong expertise in Computer Vision (CV) to build intelligent systems that can interpret architectural drawings in DWG format. The role emphasizes designing and training computer vision pipelines — from classical CV methods to state-of-the-art deep learning models — to extract geometry, text, symbols, and structural information from technical drawings. While CAD format familiarity is helpful, deep CV expertise is the primary requirement.

  • Develop and optimize computer vision models (classical + deep learning) for entity detection, segmentation, symbol recognition, and annotation extraction from architectural drawings.
  • Apply classical CV techniques (e.g., edge detection, contour analysis, Hough transform, morphological operations) alongside deep learning models to solve vector and raster understanding tasks.
  • Design and train deep learning models (e.g., CNNs, Mask R-CNN, U-Net, YOLO, DETR, Vision Transformers) for detection and segmentation of CAD drawing elements.
  • Implement OCR pipelines for text and dimension extraction in drawings.
  • Build robust data pipelines: preprocessing DWG files, rasterization/vectorization, augmentation, and dataset creation for supervised training.
  • Benchmark, evaluate, and continuously improve model accuracy, robustness, and efficiency.
  • Collaborate with cross-functional teams to integrate vision models into design automation and CAD/BIM workflows.
Qualifications

EDUCATION & BACKGROUND

  • Bachelor's, Master's, or PhD in Computer Science, Artificial Intelligence, Computer Vision, or related fields.
  • Strong foundation in mathematics, geometry, and image processing.

COMPUTER VISION EXPERTISE (PRIORITY)

  • 3+ years of hands-on experience building CV pipelines and production-ready ML models.
  • Proven track record with classical CV algorithms (OpenCV, scikit-image): contour/edge detection, shape matching, geometric transformations, Hough transform, morphological filtering.
  • Strong experience training and deploying deep learning CV models: CNNs, U-Net, Mask R-CNN, Faster R-CNN, YOLO, DETR, Vision Transformers, SAM, etc.
  • Experience with OCR (e.g., Tesseract, deep-learning-based text recognition).
  • Practical experience in combining classical CV with deep learning for hybrid solutions.

TECHNICAL SKILLS

  • Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
  • Strong engineering practices: Git, CI/CD, testing, Docker, and scalable inference deployment.
  • Familiarity with vector graphics, CAD data formats (DWG/DXF), and computational geometry is a plus, but not mandatory.

PREFERRED SKILLS

  • Knowledge of geometric deep learning or graph-based approaches for structured vector data.
  • Experience with annotation tools, dataset creation, and augmentation for CV tasks.
  • Familiarity with AEC (Architecture, Engineering, Construction) workflows is an advantage.
About Us

Established in 2023, Genia is dedicated to empowering the North American real estate market with generative AI. Our product, Structural CoPilot, automates the generation of structural engineering design drawings for the construction sector, enhancing efficiency and quality for engineering design firms and real estate developers.

The founding team has a deep background in the architecture and AI industries, with experience from leading internet and architectural engineering companies such as Amazon, Tencent, and ARUP. Team members hold degrees from renowned universities, including Yale, UPenn, Columbia, CMU, Duke, UCLA, and UBC. They have a proven track record of building multiple AI products from the ground up.

In early 2024, Genia successfully closed a multi-million dollar financing round with investors including a top-tier Silicon Valley venture capital firm and Europe's largest construction technology fund. We have also established strategic partnerships with several North American architectural engineering firms. The company is in a phase of rapid expansion and offers a competitive compensation package, including equity incentives for outstanding talent. We sincerely invite talented individuals from all backgrounds to join us!