Data Labeling Associate

Job Responsibilities

The ideal candidate will have a foundational understanding of machine learning, data annotation, quality assurance, and natural language processing. They will play a pivotal role in updating our machine learning models and ensuring their efficacy.

Machine Learning Model Updates

• Update training and test model databases with new or amended synthetic textual and image data.
• Modify and refine machine learning data creation, annotation, and rating guidelines.

Model Training and Evaluation

• Initiate model training processes using internal tools and command-line interfaces.
• Evaluate the performance of trained models to gauge their efficacy and readiness for deployment.

Data Management and Annotation

• Design and develop test and training datasets as per the criteria provided by the project manager and other full-time employees.
• Handle data efficiently, ensuring its integrity throughout the workflow.
• Engage in data relevance tasks, ensuring data sets are aligned with project goals.
• Annotate data accurately, ensuring it adheres to set guidelines.

Quality Assurance and Analysis

• Conduct manual quality analysis of model results.
• Recognize error patterns and report anomalies for further investigation.
• Deliver detailed reports on findings, including aspects such as utterance quality, LLM evaluation, ASR bug tracking, and customer pain points to be reviewed by the User Experience Research team.
• Implement basic quality control measures and ensure the reliability of processed data.
• Utilize intermediate data analysis techniques to extract insights and inform decision-making.
• Arbitrate discrepancies effectively, ensuring consistent data quality.

Linguistic and NLP Tasks

• Apply basic knowledge of natural language processing and linguistics to data processing tasks.
• Ensure linguistic accuracy in all processed and annotated data.