3D Machine Learning Engineer
Irvine, CA · On-site
Work closely with the labeling and data operations teams to define robust data annotation ... Our teams span AI, software, robotics engineering, product, field deployment, and technical ...
Irvine, CA · On-site
Work closely with the labeling and data operations teams to define robust data annotation ... Our teams span AI, software, robotics engineering, product, field deployment, and technical ...
Irvine, CA · On-site
Work closely with the labeling and data operations teams to define robust data annotation ... Our teams span AI, software, robotics engineering, product, field deployment, and technical ...
Work closely with the labeling and data operations teams to define robust data annotation ... Our teams span AI, software, robotics engineering, product, field deployment, and technical ...
Quick apply
Work closely with the labeling and data operations teams to define robust data annotation ... Our teams span AI, software, robotics engineering, product, field deployment, and technical ...
Irvine, CA · On-site
Work closely with the labeling and data operations teams to define robust data annotation ... Our teams span AI, software, robotics engineering, product, field deployment, and technical ...
Irvine, CA · On-site
Work closely with the labeling and data operations teams to define robust data annotation ... Our teams span AI, software, robotics engineering, product, field deployment, and technical ...
$53.7K - $67.5K
2% of jobs
$67.5K - $81.3K
9% of jobs
$91K is the 25th percentile. Wages below this are outliers.
$81.3K - $95.1K
20% of jobs
$95.1K - $108.9K
4% of jobs
$108.9K - $122.7K
4% of jobs
$122.7K - $136.5K
1% of jobs
$136.5K - $150.3K
0% of jobs
$150.3K - $164.1K
0% of jobs
The median wage is $171K / yr.
$164.1K - $177.9K
18% of jobs
$177.9K - $191.7K
0% of jobs
$197.1K is the 75th percentile. Wages above this are outliers.
$191.7K - $205.5K
41% of jobs
$53.7K
$153.8K
$205.5K
One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.
To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.
A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.
Full-time
Posted 18 days ago
Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing, including point cloud analysis, object detection, segmentation, and scene understanding.
Train, optimize, and deploy deep learning models using frameworks such as PyTorch or TensorFlow on cloud platforms like AWS.
Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.