1

Data Tagger Jobs (NOW HIRING)

Senior, ML Engineer - Auto Tagger

$107K - $146K/yr

The Senior ML Engineer - Auto Tagger will be responsible for architecting and optimizing data ... Architect and optimize distributed data pipelines to process massive multi-sensor logs (camera ...

Senior, ML Engineer - Auto Tagger

Ann Arbor, MI · On-site

$102K - $140K/yr

Responsibilities : • Architect and optimize distributed data pipelines to process massive multi-sensor logs (camera, LiDAR, radar, kinematics), automatically extracting and cataloging safety ...

Senior, ML Engineer - Auto Tagger

Ann Arbor, MI · On-site

$102K - $140K/yr

The Auto Tagger team is the engine behind our data flywheel, responsible for translating petabytes of raw, multi-modal vehicle data into a highly curated library of critical driving scenarios. By ...

Senior, ML Engineer - Auto Tagger

Ann Arbor, MI · On-site +1

$102K - $140K/yr

The Auto Tagger team is the engine behind our data flywheel, responsible for translating petabytes of raw, multi-modal vehicle data into a highly curated library of critical driving scenarios. By ...

Operations Apprentice II

Burlington, KS

$14.25 - $18.50/hr

... tagger and fire brigade. * The Operations Apprentice II primary responsibility is to learn building ... Review all operating data in the assigned work area to assure safe operations. Effectively ...

Operations Apprentice II

Burlington, KS · On-site

$14.25 - $18.50/hr

... tagger and fire brigade. * The Operations Apprentice II primary responsibility is to learn building ... operating data in the assigned work area to assure safe operations. • Effectively communicate ...

Operations Apprentice II

Burlington, KS

$14.25 - $18.50/hr

... tagger and fire brigade. * The Operations Apprentice II primary responsibility is to learn building ... Review all operating data in the assigned work area to assure safe operations. Effectively ...

Understanding of data privacy laws and their implications for the educational community Work Environment: The work environment characteristics described here are representative of those a staff ...

next page

Showing results 1-20

Data Tagger information

How to make $1000 a week remote?

A data tagger can potentially earn $1000 a week remotely by working multiple freelance or part-time projects, often requiring strong attention to detail and familiarity with tagging tools. Increasing workload, gaining experience, and securing higher-paying contracts can help reach this income level. Consistent effort and building a reputation can improve earning potential in this field.

What is a data tagging job?

A data tagging job involves labeling or annotating data, such as images, videos, or text, to help machine learning models understand and process information. Data taggers typically use specialized tools and need attention to detail to ensure accurate annotations, which are essential for training AI systems. The work can often be done remotely and may require basic technical skills and familiarity with data management software.

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

To thrive as a Data Tagger, you need keen attention to detail, strong analytical skills, and familiarity with labeling guidelines, often backed by a high school diploma or relevant experience. Proficiency with data annotation platforms, spreadsheets, and sometimes basic knowledge of programming or machine learning tools is common. Effective communication, reliability, and the ability to concentrate for extended periods help someone excel in this role. These skills ensure that datasets are accurately labeled, which is critical for training high-quality AI and machine learning models.

What are Data Taggers?

Data Taggers are professionals who label or annotate data, such as images, text, audio, or video, to make it usable for machine learning models. Their work involves identifying and marking specific features or objects within datasets, ensuring that artificial intelligence systems can learn from accurately labeled information. Data tagging is critical in training algorithms for tasks like image recognition, natural language processing, and autonomous driving. Attention to detail and consistency are important skills for this role.

What job makes $10,000 a month without a degree?

A data tagger typically does not earn $10,000 a month; most such roles pay hourly or per project and require minimal formal education. High earnings in data-related fields usually involve specialized skills, experience, or working in roles like data scientists or freelance consultants, which often require advanced training or certifications. Achieving $10,000 monthly income without a degree is uncommon in entry-level data tagging jobs.

What does a data tagger do?

A data tagger is responsible for labeling or annotating data, such as images, text, or videos, to help train machine learning models. This role requires attention to detail and often involves using specialized tools or software to ensure accurate tagging, which is essential for improving AI system performance.

What is the difference between Data Tagger vs Data Annotator?

AspectData TaggerData Annotator
CredentialsHigh school diploma or equivalent; basic computer skillsHigh school diploma or equivalent; attention to detail
Work EnvironmentData labeling platforms, remote or on-siteData labeling platforms, remote or on-site
Industry UsageAI, machine learning, data processingAI, machine learning, data processing
Search & Comparison IntentYesYes

Both Data Taggers and Data Annotators work in AI and machine learning fields, often performing similar tasks like labeling data. The main difference lies in terminology used by different companies or platforms, but their roles, skills, and work environments are largely overlapping. Understanding these terms can help job seekers find relevant opportunities in data labeling and annotation roles.

What are some common challenges faced by Data Taggers and how can they be managed?

Data Taggers often encounter challenges such as repetitive tasks, maintaining high accuracy, and meeting tight deadlines. To manage these, it's important to develop strong attention to detail and take regular breaks to avoid fatigue. Collaborative communication with team members and supervisors can also help clarify ambiguous data points and improve overall data quality. Many organizations provide training sessions and quality assurance feedback to support continuous improvement in this role.
More about Data Tagger jobs
What cities are hiring for Data Tagger jobs? Cities with the most Data Tagger job openings:
What states have the most Data Tagger jobs? States with the most job openings for Data Tagger jobs include:
Infographic showing various Data Tagger job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 100% Remote job distribution.
Senior, ML Engineer - Auto Tagger

Senior, ML Engineer - Auto Tagger

Torc Robotics

Remote

$107K - $146K/yr

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Job Summary:
Torc Robotics is a leader in autonomous driving technology focused on developing software for automated trucks. The Senior ML Engineer - Auto Tagger will be responsible for architecting and optimizing data pipelines, developing ML-assisted algorithms, and collaborating across teams to enhance data curation for autonomous trucking.
Responsibilities:
• Scenario Mining at Scale: Architect and optimize distributed data pipelines to process massive multi-sensor logs (camera, LiDAR, radar, kinematics), automatically extracting and cataloging safety-critical and long-tail driving events.
• Advanced Event Tagging: Develop and tune both heuristic-based and ML-assisted algorithms (including exploring Vision-Language Models or semantic vector search) to automatically classify and describe complex environmental and behavioral scenarios.
• Standardized Data Structuring: Extract and format scenario data utilizing the Pegasus layer standard (alongside opensource frameworks) to ensure semantic consistency and rigorous metadata integrity.
• Data Flywheel Integration: Manage the ingestion of tagged events into the observations database, enabling high-speed querying and retrieval for ML training, regression testing, and system validation.
• Cross-Functional Alignment: Operate with broad autonomy to drive consensus across organizational boundaries. Collaborate closely with downstream consumers in perception, simulation, and systems engineering to define what constitutes an "interesting scenario" and operationalize a continuous data loop.
• Mentorship & Team Growth: Guide, mentor, and elevate less-experienced engineers. Lead design reviews, establish coding standards, and foster a culture of technical excellence and collaborative problem-solving.
Qualifications:
Required:
• BS or MS in Computer Science, Robotics, Engineering, or a STEM field, with 6+ years in data engineering, ML systems, or autonomous data curation.
• Core Languages: Strong Python and SQL skills, with heavy experience processing massive time-series or unstructured datasets.
• ML & Dataset Curation: Hands-on machine learning and dataset curation experience, with a demonstrated history of implementing targeted datasets that measurably improve downstream model performance.
• Data Exploration: Hands-on experience using Databricks (or similar platforms) for large-scale analytics, interactive querying, and making massive vehicle datasets searchable.
• Cloud & Compute: Expertise in distributed compute frameworks (Ray, Spark, Beam) and cloud platforms (AWS, GCP, or Azure) for executing heavy data workloads.
• AV Standards: Experience parsing complex data formats and applying scenario-description standards like Pegasus layers.
• Communication: Exceptional ability to translate complex data engineering challenges into clear strategies for cross-functional stakeholders.
• Technical Leadership: Proven track record of mentoring teams, driving system architecture, and defining engineering roadmaps.
Preferred:
• Auto-labeling & VLMs: Familiarity with foundational models, auto-labeling pipelines, or zero-shot classification for scenario extraction.
• Model Serving: Experience with vLLM, SGLang, or similar frameworks for highly optimized, high-throughput model serving and inference.
• Semantic Inference: Experience with semantic extraction and attribute mapping to help build out a robust semantic inference engine, moving beyond standard bounding-box object detection.
• Data Tooling: Familiarity with parsing robotics formats (ROS bags, MCAP) and optimizing high-performance columnar storage formats (Parquet, Arrow).
• Downstream Integration: Knowledge of how scenario data feeds into generative simulation workflows, neural rendering, or sensor fusion validation.
• Advanced Retrieval: Experience building semantic retrieval systems or vector databases for automotive data.
Company:
Torc provides L4 end-to-end self-driving software for mobility, trucking, mining, and defense markets through strategic partnerships Founded in 2005, the company is headquartered in Blacksburg, USA, with a team of 501-1000 employees. The company is currently Late Stage.