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Data Labeling For Autonomous Vehicle Jobs (NOW HIRING)

Stack AV

Contract Labeling Associate

Pittsburgh, PA · On-site

The Labeling team delivers algorithms, tools and infrastructure to provide data labels that can be ... and spatial data annotation activities crucial for autonomous vehicle navigation, utilizing ...

WME Express

Autonomous Vehicle Operator

Daly City, CA · On-site

$24 - $30/hr

Pay: $24-$30/hour Job Overview We're looking for Vehicle Operators to support data collection for autonomous vehicle testing. You'll be driving test vehicles and helping collect critical data that ...

WME Express

Autonomous Vehicle Operator

Daly City, CA · On-site

Pay: $24-$30/hour Job Overview We're looking for Vehicle Operators to support data collection for autonomous vehicle testing. You'll be driving test vehicles and helping collect critical data that ...

aap3

Data Engineer - Autonomous Vehicle

Los Altos, CA · On-site

$135K - $162K/yr

Data Engineer with strong AWS + Python/SQL, who builds scalable pipelines for ML training and has ... Nice-to-have experience includes autonomous vehicle or robotics sensor data, simulation pipelines ...

aap3

Data Engineer - Autonomous Vehicle

Los Altos, CA

$135K - $162K/yr

Data Engineer with strong AWS + Python/SQL, who builds scalable pipelines for ML training and has ... Nice-to-have experience includes autonomous vehicle or robotics sensor data, simulation pipelines ...

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Data Labeling For Autonomous Vehicle information

What is data labeling for autonomous vehicles?

Data labeling for autonomous vehicles involves annotating images, videos, or sensor data to help machine learning models understand and interpret the environment around the vehicle. This can include tagging objects like cars, pedestrians, traffic signs, and lanes to train algorithms for perception and decision-making. Accurate data labeling is crucial for improving the safety and performance of self-driving systems, as it ensures the models can recognize and react to real-world scenarios. Typically, data labelers use specialized software to draw bounding boxes, segment objects, or classify items in sensor data. The quality of labeled data directly impacts the reliability of autonomous vehicle technology.

What are some common challenges faced by data labelers working in the autonomous vehicle industry?

Data labelers for autonomous vehicles often encounter challenges such as ensuring high accuracy and consistency when annotating complex road scenes, especially with rapidly changing environments and diverse object types like pedestrians, vehicles, and traffic signs. The work can be repetitive and requires strong attention to detail to minimize errors that could impact the performance of self-driving systems. Additionally, labelers may need to collaborate closely with engineers and quality assurance teams to address ambiguities and continuously update labeling guidelines as technology advances.

What are the key skills and qualifications needed to thrive as a Data Labeling Specialist for Autonomous Vehicles, and why are they important?

To thrive as a Data Labeling Specialist for Autonomous Vehicles, you need strong attention to detail, familiarity with computer vision concepts, and a background in data management or related fields. Experience with annotation tools (such as Labelbox, CVAT, or Supervisely), image/video editing software, and sometimes basic programming or scripting is often required. Excellent concentration, patience, and communication skills help ensure accuracy and effective collaboration with engineering teams. These skills are vital because high-quality labeled data directly impacts the performance and safety of autonomous vehicle systems.
Infographic showing various Data Labeling For Autonomous Vehicle job openings in the United States as of May 2026, with employment types broken down into 73% Full Time, 7% Part Time, and 20% Contract. Highlights an 100% In-person job distribution.

Contract Labeling Associate

Stack AV

Pittsburgh, PA • On-site

Other

Posted 2 days ago

Job description

About the Role:

The Labeling team delivers algorithms, tools and infrastructure to provide data labels that can be used by Perception, Motion Planning, and other ML teams for training and evaluation. They work closely with manual labeling efforts and infrastructure teams to create a data centric ecosystem needed to develop real time, safety critical ML models for autonomous driving. 

We are looking for a Labeling Analyst to review and ensure a high quality of labels delivered to our Autonomy teams. The job entails operating internal and third party tools to visualize labels and checking if they meet our quality and labeling standards. The Labeling Analyst will also collaborate across operational and technical teams to identify and correct issues and support other labeling related tasks as needed.

Responsibilities:

  • Review labels using internal and third party tooling to identify defects, mis-labeled or unlabeled items.
  • Perform high-precision mapping and spatial data annotation activities crucial for autonomous vehicle navigation, utilizing proprietary tooling.
  • Update issue trackers based on defects found, summarize findings and write reports.
  • Follow up with internal and third party partners to resolve quality issues.
  • Participate in defining requirements for, testing, and creating user documentation for internal label editing and review tooling.
  • Help with other tasks such as selecting suitable logs for labeling and generating analytics on the QA process.
  • Partner with engineering teams to define requirements for, test, and create user documentation for new proprietary labeling and mapping tools.

Qualifications: 

  • Computer literacy and operate web based tools.
  • Ability to work with Google docs, spreadsheets, write summary reports and aggregate statistics using Google Sheets.
  • Ability to take high level acceptance criteria guidance and follow it to execute on label review.
  • Ability to work in a highly collaborative, team environment across a variety of functional domains, both operational and technical.

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