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Data Labeling Autonomous Jobs (NOW HIRING)

Zoox

Operations Lead - Perception Data Labeling

Boston, MA · On-site

$163K - $223K/yr

... Labeling team. The team is responsible for training and evaluation data powering the perception ... autonomous vehicle sensor modalities (LiDAR, Radar, Camera, Thermal) and their respective data ...

Zoox

Operations Lead - Perception Data Labeling

Boston, MA · On-site

$163K - $223K/yr

... Labeling team. The team is responsible for training and evaluation data powering the perception ... autonomous vehicle sensor modalities (LiDAR, Radar, Camera, Thermal) and their respective data ...

Stack AV

Contract Labeling Associate

Pittsburgh, PA · On-site

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 ...

Stack AV

Contract Labeling Associate

Pittsburgh, PA · On-site

The Labeling team delivers algorithms, tools and infrastructure to provide data labels that can be ... Perform high-precision mapping and spatial data annotation activities crucial for autonomous ...

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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.

Operations Lead - Perception Data Labeling

Zoox

Boston, MA • On-site

$163K - $223K/yr

Full-time

Medical, Life, PTO

Posted 29 days ago

Job description

We are seeking an experienced and highly skilled operations leader 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 ML engineers, product owners, data engineers, and external partners to scope and deliver high-quality training and evaluation data to help the Zoox Perception AI team meet critical deadlines.
In this role, you will:
  • Translate and coalesce company-wide feature requirements into concrete and comprehensive data deliverables
    • >
  • Work collaboratively with AI teams, project management, and data annotation teams to manage the collection and labeling of training and evaluation data powering Zoox's AI perception stack.
    • >
  • Manage vendor allocation and budgeting in conjunction with the milestone and release timelines at Zoox.
    • >
  • Define and maintain clear tracking of outcomes, risk, and data quality to ensure transparency and accountability.
    • >

Qualifications:
  • Experience in Technical Program Management or Operations Strategy: 5+ years of experience managing complex, cross-functional programs within the technology, autonomous driving, or AI/ML sectors.
  • Perception & Sensor Domain Knowledge: Strong understanding of the machine learning development lifecycle (MLDL) and familiarity with autonomous vehicle sensor modalities (LiDAR, Radar, Camera, Thermal) and their respective data annotation requirements.
  • Strategic Capacity Planning: Demonstrated ability to translate high-level product roadmaps and engineering requirements into granular operational execution plans. Experience managing resource allocation and throughput for large-scale workflows.
  • Vendor Ecosystem Understanding: Experience overseeing large-scale vendor operations or managed service providers, specifically regarding quality, latency, and volume targets.
  • Data Literacy: Proficiency with SQL or Python/Pandas to query databases, analyze throughput metrics, and generate reports without needing engineering support.

$163,000 - $223,000 a year
Base Salary Range
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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