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3D Annotation Jobs in Texas (NOW HIRING)

OpenSpace

Sr. Computer Vision Engineer

Austin, TX

$152K - $220K/yr

Develop and optimize image localization, SLAM, SfM, and 3D reconstruction techniques. * Implement ... Oversee dataset creation, curation, and annotation to ensure high-quality training and validation ...

New

LJ Cross Plumbing

Plumbing Detailer

Austin, TX · On-site

... and 3D models that support high-quality production and streamlined project execution. Key Responsibilities: Revit Modeling & Annotation: Annotate Revit drawings with pipe sizes, elevations ...

BGE, Inc.

Transportation Designer

Houston, TX · On-site

Use MicroStation to perform accurate drafting and annotation for the development of plans and ... Ability to work between MicroStation, AutoCAD, and Civil 3D is a plus. * Excellent teamwork and ...

INK Civil

Be Seen First

CAD Technician

San Antonio, TX · On-site

$23 - $29/hr

Working knowledge of CAD standards, Civil 3D features, annotation styles, line styles, sheet set manager, and drawing setup * Collaborate with engineers and senior designers on project tasks

INK Civil

Be Seen First

CAD Technician

San Marcos, TX · On-site

$23 - $29/hr

Working knowledge of CAD standards, Civil 3D features, annotation styles, line styles, sheet set manager, and drawing setup * Collaborate with engineers and senior designers on project tasks

BGE

Transportation Designer

Houston, TX · On-site

Use MicroStation to perform accurate drafting and annotation for the development of plans and ... Ability to work between MicroStation, AutoCAD, and Civil 3D is a plus. * Excellent teamwork and ...

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3D Annotation information

What is 3D annotation?

3D annotation is the process of labeling or tagging objects, features, or areas within three-dimensional data, such as point clouds, meshes, or 3D images. It is commonly used in fields like autonomous driving, robotics, and augmented reality to help machine learning models understand and interpret 3D environments. Annotators may identify and classify objects, draw bounding boxes, or segment specific regions within the 3D space. This data is crucial for training and validating AI systems to recognize and interact with real-world objects accurately.

What are the key skills and qualifications needed to thrive as a 3D Annotation Specialist, and why are they important?

To excel as a 3D Annotation Specialist, you need attention to detail, spatial awareness, and basic knowledge of 3D modeling or computer vision, often supported by a background in computer science or related fields. Familiarity with 3D annotation tools like Supervisely, CVAT, or Labelbox, and experience with relevant file formats and data management systems, are typically required. Strong problem-solving skills, patience, and effective communication help individuals stand out in this meticulous, collaborative role. These skills ensure accurate data labeling, which is crucial for the development and training of reliable AI and machine learning models.

What are the typical challenges faced when working in 3D annotation roles, and how can they be addressed?

Professionals in 3D annotation often encounter challenges such as maintaining accuracy while labeling complex objects from multiple angles and ensuring consistency across large datasets. The work can be repetitive, requiring strong attention to detail and patience to avoid errors that may impact downstream machine learning models. Collaboration with data scientists, software engineers, and QA teams is essential to clarify guidelines and resolve ambiguities. Adopting efficient annotation tools and proactive communication within the team can help overcome these challenges and improve workflow efficiency.
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3D Annotation jobs near you
Infographic showing various 3D Annotation job openings in Texas as of June 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 95% In-person, and 5% Remote job distribution.

Software Research Engineer - Machine Learning

Avride

Austin, TX • On-site

$203K/yr

Other

Posted yesterday

Job description

About the team

Avride is at the forefront of autonomous mobility, developing and deploying state-of-the-art self-driving cars and delivery robots. We're shaping the future of transportation and logistics-and our Labeling Team plays a vital role in bringing that vision to life.

The Labeling Backend Team builds the data infrastructure that powers groundbreaking research and development across our labeling pipelines, data preparation workflows, and model training processes. The high-quality labeled data we deliver is critical to advancing our core technologies and supports the diverse range of models that drive our entire business.

About the role

We are looking for a Research Engineer to improve the quality and representativeness of datasets powering our self-driving systems. You will design algorithms and tools for auto-labeling, data mining and dataset monitoring, combining strong Python engineering with applied ML concepts. Your work will directly enhance data efficiency, reduce labeling costs, and improve model performance.

What you'll do
  • Design and implement algorithms that optimize annotation, including auto-labeling systems that reduce manual effort and increase throughput
  • Build data-mining and active-learning pipelines to surface the highest-value samples for training
  • Create dataset-quality monitoring systems identifying noise, redundancy, and low-value data
  • Develop analytics platforms (databases, dashboards, reporting) to track dataset quality and coverage over time
  • Collaborate with ML and Perception teams to integrate research results into production workflows
  • Explore emerging approaches (vision-language models, weak supervision, uncertainty estimation) to expand dataset quality and automation
What you'll need
  • Bachelor's or Master's degree in Computer Science or related field
  • Strong Python skills for algorithm development and prototyping
  • Solid understanding of ML concepts (metrics, evaluation, dataset sampling, etc.)
  • Experience with data processing and analysis at scale
  • Ability to move between research prototyping and production engineering
  • Strong analytical mindset and curiosity to dig deep into data quality problems
Nice to have
  • Experience with auto-labeling, weak supervision, or human-in-the-loop ML
  • Exposure to 3D data (point clouds, sensor fusion, 3D annotation pipelines)
  • Background in AV, robotics, or large-scale ML dataset development
  • Experience with foundation models / VLMs
  • Workflow orchestration systems (Argo, Airflow, etc.)
  • Backend engineering experience (APIs, ORMs, databases)
  • Experience building dashboards or analytics systems (Grafana, Superset, etc.)

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