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Data Labeling Autonomous Jobs (NOW HIRING)

hireVouch

Forward Deployed Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Frontier Data Labeling Service: Specialized data labeling through Aligner, leveraging subject ... You'll know exactly what you're responsible for and have the autonomy to execute. We empower people ...

Hirevouch

Forward Deployed Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Frontier Data Labeling Service : Specialized data labeling through Aligner, leveraging subject ... You'll know exactly what you're responsible for and have the autonomy to execute. We empower people ...

General Motors

Senior AI/ML Engineer

Sunnyvale, CA · On-site +1

$122K - $168K/yr

The Data Labeling Engineering team designs, builds, and operates hybrid human/machine data labeling tools and pipelines that power autonomous vehicle machine learning models within General Motors' AV ...

General Motors

Senior AI/ML Engineer

Sunnyvale, CA · On-site +1

$124K - $170K/yr

The Data Labeling Engineering team designs, builds, and operates hybrid human/machine data labeling tools and pipelines that power autonomous vehicle machine learning models within General Motors' AV ...

General Motors

Senior AI/ML Engineer

Sunnyvale, CA · On-site

$122K - $168K/yr

The Data Labeling Engineering team designs, builds, and operates hybrid human/machine data labeling tools and pipelines that power autonomous vehicle machine learning models within General Motors' AV ...

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Data Labeling Autonomous information

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

To thrive as a Data Labeling Autonomous Specialist, you need attention to detail, a strong understanding of data annotation protocols, and familiarity with data labeling best practices, often supported by relevant technical or analytical experience. Proficiency in data labeling tools such as Labelbox, Supervisely, or CVAT, and sometimes experience with Python or other scripting languages, is typically required. Strong organizational skills, focus, and the ability to work independently make someone stand out in this position. These skills and qualities are important because accurate data labeling directly impacts the quality of machine learning models and overall project success.

What is the difference between Data Labeling Autonomous vs Data Labeling Specialist?

AspectData Labeling AutonomousData Labeling Specialist
CredentialsTypically no formal certifications required; familiarity with labeling tools beneficialOften requires basic training or certifications in data annotation
Work EnvironmentPrimarily remote, independent work with automation toolsCan be remote or on-site, often supervised or team-based
Industry UsageUsed in AI/ML projects for autonomous data processingCommon in data annotation firms and tech companies

Data Labeling Autonomous focuses on automated or semi-automated labeling processes, requiring minimal manual intervention, while Data Labeling Specialist involves manual annotation tasks with more direct human input. Both roles are essential in AI development but differ in automation level and work setup.

What are common challenges faced by Data Labeling Autonomous professionals, and how can they be addressed?

Data Labeling Autonomous professionals often encounter challenges such as handling ambiguous or edge-case data, maintaining consistency across large datasets, and meeting tight deadlines while ensuring high-quality annotations. These challenges can be addressed by adhering to clear labeling guidelines, participating in regular team calibration sessions, and using annotation tools that support quality checks. Collaboration with machine learning engineers and project managers also helps clarify requirements and resolve uncertainties, contributing to more accurate and useful labeled data.

What is data labeling in the context of autonomous systems?

Data labeling for autonomous systems involves assigning meaningful tags or labels to raw data—such as images, video, or sensor readings—so that machine learning models can recognize and interpret different objects, actions, or scenarios. This process is crucial for training autonomous vehicles or robots to accurately detect pedestrians, road signs, obstacles, and other relevant elements in their environment. High-quality data labeling ensures that autonomous systems can make safe and reliable decisions in real-world situations.
Infographic showing various Data Labeling Autonomous job openings in the United States as of May 2026, with employment types broken down into 75% Full Time, and 25% Part Time. Highlights an 50% In-person, and 50% Remote job distribution.

Senior Data Scientist - Data for Perception Machine Learning

Zoox

Foster City, CA • On-site

$190K - $258K/yr

Full-time

Medical, Life, PTO

Posted 13 days ago

Job description

We are seeking an experienced and highly skilled data scientist to join the Perception Data and Labeling team.. The team is responsible for training and evaluation data powering the perception (vision, lidar, and other modalities) ML models at Zoox. The candidate will work alongside data ops partners, ML engineers, software developers, and data engineers to improve model performance through high quality human- and auto-labeled data.
In this role, you will:
  • Define and implement scalable data quality measures across complex, multimodal data labeling pipelines
  • Drive data-centric ML model improvements to achieve critical Zoox milestones
  • Support an org-wide data ontology and class structure for perception models
  • Determine trade-offs and integrations between human-labeled, human-in-the-loop, and zero-shot autolabeled data
  • Build metrics to quantify labeling throughput, capacity, and annotator/vendor quality
Qualifications:
  • Master's or PhD degree in a field relevant to autonomous driving (computer science, robotics) to the analysis of human data (computational neuroscience, cognitive science) or a related field
  • Proficient using data query languages (SQL and/or Spark/scala) to quickly build complex yet efficient data queries at scale and using Python to build production-quality code
  • Proficient in exploratory data analysis (EDA) and data visualization to understand and present trends and their implications for the business.
  • Background in statistical modeling and analysis; including experience making data-driven decisions that connect point and uncertainty estimates to business impact.
  • Experience with data-centric ML development and data curation
Bonus Qualifications:
  • Experience with experiment design and statistical comparisons (A/B testing, parametric/non-parametric statistics, etc.)
  • Experience with human data collection, including annotation task design
$190,000 - $258,000 a year
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
 
Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. 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.
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