Work within annotation platforms (e.g., Encord, CVAT, Label Studio, or similar) to complete labeling tasks efficiently. * Document and communicate recurring issues or ambiguities in the data to ...
Quick apply
Work within annotation platforms (e.g., Encord, CVAT, Label Studio, or similar) to complete labeling tasks efficiently. * Document and communicate recurring issues or ambiguities in the data to ...
Work within annotation platforms (e.g., Encord, CVAT, Label Studio, or similar) to complete labeling tasks efficiently. * Document and communicate recurring issues or ambiguities in the data to ...
Quick apply
Work within annotation platforms (e.g., Encord, CVAT, Label Studio, or similar) to complete labeling tasks efficiently. * Document and communicate recurring issues or ambiguities in the data to ...
Work within annotation platforms (e.g., Encord, CVAT, Label Studio, or similar) to complete labeling tasks efficiently. * Document and communicate recurring issues or ambiguities in the data to ...
Quick apply
Work within annotation platforms (e.g., Encord, CVAT, Label Studio, or similar) to complete labeling tasks efficiently. * Document and communicate recurring issues or ambiguities in the data to ...
Senior AI/ML Engineer
Phoenix, AZ · On-site +1
$103K - $142K/yr
Apply ML to labeling itself Collaborate with ML engineers to design and integrate ML-driven data annotation (pre-labeling, autolabeling, active learning loops), helping us move from human-only to ...
Senior AI/ML Engineer
Phoenix, AZ · On-site +1
$103K - $142K/yr
Apply ML to labeling itself Collaborate with ML engineers to design and integrate ML-driven data annotation (pre-labeling, autolabeling, active learning loops), helping us move from human-only to ...
... annotation to delivery. We design and create datasets from scratch, recruit and manage the domain experts who evaluate model output, and run everything through our own platform, Label Studio, the ...
Quick apply
... annotation to delivery. We design and create datasets from scratch, recruit and manage the domain experts who evaluate model output, and run everything through our own platform, Label Studio, the ...
Senior Data Engineer / Data Curator
$130K - $177K/yr
Experience with data annotation tools and platforms for manual or semi-automated labeling. * Experience with NLP data formats, such as JSONL, text, or embeddings, and an understanding of tokenization.
Senior Data Engineer / Data Curator
$130K - $177K/yr
Experience with data annotation tools and platforms for manual or semi-automated labeling. * Experience with NLP data formats, such as JSONL, text, or embeddings, and an understanding of tokenization.
Senior Data Engineer / Data Curator
Phoenix, AZ · On-site
$130K - $177K/yr
Experience with data annotation tools and platforms for manual or semi-automated labeling. * Experience with NLP data formats, such as JSONL, text, or embeddings, and an understanding of tokenization.
Senior Data Engineer / Data Curator
Phoenix, AZ · On-site
$130K - $177K/yr
Experience with data annotation tools and platforms for manual or semi-automated labeling. * Experience with NLP data formats, such as JSONL, text, or embeddings, and an understanding of tokenization.
Senior Data Engineer / Data Curator
Phoenix, AZ · On-site
$130K - $177K/yr
Experience with data annotation tools and platforms for manual or semi-automated labeling. * Experience with NLP data formats, such as JSONL, text, or embeddings, and an understanding of tokenization.
Senior Data Engineer / Data Curator
Phoenix, AZ · On-site
$130K - $177K/yr
Experience with data annotation tools and platforms for manual or semi-automated labeling. * Experience with NLP data formats, such as JSONL, text, or embeddings, and an understanding of tokenization.
Annotation Labelling information
What is annotation labelling?
Annotation labelling is the process of tagging or marking data—such as images, text, or audio—with relevant information or labels. This is an essential step in preparing datasets for machine learning and artificial intelligence models, as it helps algorithms understand and learn from raw data. Annotation labelling can include tasks like identifying objects in photos, transcribing speech, or categorizing text. Skilled annotators ensure accuracy and consistency to improve model performance. People in this role often use specialized tools or software to streamline and standardize the annotation process.
What are the key skills and qualifications needed to thrive as an Annotation Labelling Specialist, and why are they important?
To thrive as an Annotation Labelling Specialist, you need strong attention to detail, data analysis capabilities, and familiarity with data annotation standards, usually supported by a background in computer science or related fields. Proficiency with annotation tools such as Labelbox, CVAT, or Supervisely, and sometimes knowledge of basic programming or scripting, is typically required. Excellent communication, consistency, and the ability to follow complex instructions are crucial soft skills for producing high-quality labeled data. These skills ensure the accuracy and reliability of datasets, which are foundational for successful machine learning and AI model development.
What are some common challenges faced by Annotation Labelling professionals, and how can they be managed?
Annotation Labelling professionals often encounter challenges such as maintaining high accuracy while handling repetitive data, meeting tight deadlines, and adapting to evolving project guidelines. To manage these, it’s important to develop strong attention to detail, regularly communicate with team leads to clarify instructions, and leverage annotation tools efficiently. Collaborating closely with quality assurance teams can also help identify and correct errors early, ensuring consistently high-quality outputs.
What is the difference between Annotation Labelling vs Data Labeling Specialist?
| Aspect | Annotation Labelling | Data Labeling Specialist |
|---|---|---|
| Credentials | Basic technical skills, attention to detail | Similar skills, sometimes additional domain knowledge |
| Work Environment | Data annotation platforms, remote or office | Data annotation tasks, often remote or in-office |
| Industry Usage | AI, machine learning, autonomous vehicles | AI, machine learning, healthcare, retail |
| Search & Comparison | Commonly compared for entry-level data tasks | Related but broader role |
Annotation Labelling involves marking data such as images, text, or videos to train AI models. Data Labeling Specialists perform similar tasks but may have a broader scope, including verifying and managing labeled data. Both roles are essential in AI development, often overlapping in skills and work environment, but Annotation Labelling is more focused on the annotation process itself.
What are popular job titles related to Annotation Labelling jobs in Arizona? For Annotation Labelling jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Annotation Labelling jobs in Arizona look for? The top searched job categories for Annotation Labelling jobs in Arizona are:
What cities in Arizona are hiring for Annotation Labelling jobs? Cities in Arizona with the most Annotation Labelling job openings:
$25/hr
Full-time
Posted 6 days ago
Job description
Location: Remote Employment Type: Full-Time | 40 hours/week Compensation: $25/hour
About the Role:
We are looking for a detail-oriented and technically capable Vision-Language-Action (VLA) Annotator to join our data operations team in Phoenix, Arizona. In this role, you will be responsible for reviewing, labeling, and quality-checking multimodal datasets used to train and evaluate autonomous driving and robotics models. Your work directly impacts the safety and performance of AI systems operating in the real world.
This is a full-time, 40-hour-per-week position requiring sustained focus, sound judgment, and the ability to apply structured annotation guidelines to complex, real-world scenarios — including frequent edge cases.
Key Responsibilities:
- Review and annotate video footage, sensor telemetry, and camera feeds from autonomous vehicle test drives and robotics platforms.
- Assess vehicle and robotic behavior in 3D space using 2D camera inputs, including approach angles, following distances, trail alignment, and controlled stop quality.
- Use time-series telemetry data — including speed, throttle, steering, and braking charts — to make precise trim and segmentation decisions on data clips.
- Apply annotation guidelines consistently while exercising independent judgment on ambiguous or edge-case scenarios.
- Identify and flag unsafe, incomplete, or anomalous driving behaviors (e.g., rolling stops, improper following distance, out-of-distribution maneuvers).
- Maintain high throughput and accuracy standards; participate in regular quality audits and calibration sessions.
- Work within annotation platforms (e.g., Encord, CVAT, Label Studio, or similar) to complete labeling tasks efficiently.
- Document and communicate recurring issues or ambiguities in the data to improve pipeline quality.
Preferred Qualifications:
- Education: Bachelor's degree with a STEM background preferred (Engineering, Computer Science, Physics, Mathematics, GIS, or related field).
- Spatial & Mechanical Reasoning: Demonstrated ability to interpret vehicle or robotic behavior in 3D space from 2D camera feeds. Backgrounds in robotics, automotive engineering, mechanical engineering, GIS, or simulation are strong indicators.
- Time-Series Data Literacy: Experience reading and interpreting sensor data, telemetry charts, lab instrumentation output, or signal processing data. Comfort with chart-heavy analytical workflows is essential for making precise trim decisions.
- Driving Familiarity: Regular driving experience, ideally in varied or off-road conditions. Must be able to distinguish safe from unsafe driving behavior, recognize complete vs. rolling stops, and assess reasonable following distances.
- Detail Orientation with Tolerance for Ambiguity: Ability to follow precise, rule-based guidelines while also applying sound judgment on frequent edge cases. Prior experience in QA, data annotation, or lab/research settings is a strong signal.
- Video Review Endurance: Comfort with sustained video review tasks. Prior experience in video editing, surveillance monitoring, sports performance analysis, or media production is a plus.
Nice-To-Haves:
- Prior annotation or data labeling experience, especially in autonomy or robotics datasets.
- Familiarity with geospatial tools, map interfaces, or GIS platforms.
- Hands-on experience with Encord, Label Studio, CVAT, Scale AI, or comparable labeling platforms.
- Background in autonomous vehicles, ADAS systems, or driver safety analysis.