Sarah Woody

Key Skills

Software

Data Annotation Tech

Other

Remotasks

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Classification

Emotion Recognition

Evaluation Rating

Question Answering

Text Summarization

Freelancer Overview

I have experience as an AI Trainer at Outlier, where I specialized in training AI models in microbiology and infectious diseases. My work involved creating effective prompts, evaluating, grading, and correcting AI-generated responses to ensure accuracy and relevance. This role required me to work with diverse data labeling sources, including text, audio, and images, to curate high-quality training datasets that enhanced model performance and reliability. My unique skill set combines subject matter expertise in microbiology with hands-on experience in AI training. My attention to detail and ability to refine AI outputs through precise feedback set me apart, as I consistently delivered improvements that strengthened the AI's capabilities in interpreting complex biological data.

Labeling Experience

Genesis- Image to Text

Scale AIImageQuestion AnsweringEmotion Recognition

The Genesis Image-to-text project involved evaluating the model's ability to interpret visual content and provide accurate, contextually relevant responses. In this role, you uploaded images, posed questions related to the content or details of the images, and assessed the AI's responses for precision and contextual alignment. This work required a strong understanding of both the visual elements (depth perception and spatial reasoning) and the nuances of effective AI response evaluation, ensuring the model's ability to process and respond accurately to visual data.

The Genesis Image-to-text project involved evaluating the model's ability to interpret visual content and provide accurate, contextually relevant responses. In this role, you uploaded images, posed questions related to the content or details of the images, and assessed the AI's responses for precision and contextual alignment. This work required a strong understanding of both the visual elements (depth perception and spatial reasoning) and the nuances of effective AI response evaluation, ensuring the model's ability to process and respond accurately to visual data.

2024

Genesis- Video to Text

Scale AIVideoQuestion AnsweringText Generation

In this project, I evaluated an AI system's ability to interpret and reason based on video data. With pre-loaded videos, I analyzed their content, and posed targeted questions requiring the AI to extract and infer information from visual and other contextual elements, like spatial reasoning, depth perception, and temporal localization. The evaluation also focused on the AI's ability to recognize and interpret cues such as body language, facial expressions, actions, and scene context. I assessed the AI's responses for accuracy, relevance, and depth of understanding. The project also tested the AI's capability to process diverse video characteristics, such as varying lighting conditions, camera angles, and dynamic actions, to ensure reliability and adaptability across different visual environments.

In this project, I evaluated an AI system's ability to interpret and reason based on video data. With pre-loaded videos, I analyzed their content, and posed targeted questions requiring the AI to extract and infer information from visual and other contextual elements, like spatial reasoning, depth perception, and temporal localization. The evaluation also focused on the AI's ability to recognize and interpret cues such as body language, facial expressions, actions, and scene context. I assessed the AI's responses for accuracy, relevance, and depth of understanding. The project also tested the AI's capability to process diverse video characteristics, such as varying lighting conditions, camera angles, and dynamic actions, to ensure reliability and adaptability across different visual environments.

2024 - 2024

Dolphin Multimodal- Audio-to-Text

Scale AIAudioQuestion AnsweringText Generation

In this project, I evaluated an AI system's ability to analyze and interpret audio data. My responsibilities included uploading audio files, posing specific questions related to the content—such as assessing emotions, identifying tone, pitch, or intent—and grading the AI's responses for accuracy and relevance. These questions tested the system's capacity to recognize emotions, tone, pitch, intent, and other nuanced audio cues, as well as its ability to reason logically based on the audio's context. Additionally, I evaluated the system's ability to handle diverse audio characteristics, such as varying accents, noise levels, and speech patterns, ensuring it could provide consistent and accurate results across different scenarios. This project demanded a combination of technical understanding and analytical skills to enhance the AI's capacity for robust audio analysis and interpretation.

In this project, I evaluated an AI system's ability to analyze and interpret audio data. My responsibilities included uploading audio files, posing specific questions related to the content—such as assessing emotions, identifying tone, pitch, or intent—and grading the AI's responses for accuracy and relevance. These questions tested the system's capacity to recognize emotions, tone, pitch, intent, and other nuanced audio cues, as well as its ability to reason logically based on the audio's context. Additionally, I evaluated the system's ability to handle diverse audio characteristics, such as varying accents, noise levels, and speech patterns, ensuring it could provide consistent and accurate results across different scenarios. This project demanded a combination of technical understanding and analytical skills to enhance the AI's capacity for robust audio analysis and interpretation.

2024 - 2024

Prompt Generation

Scale AITextQuestion Answering

In this project, I evaluated an AI system's performance in answering complex and objective microbiological questions. This involved designing challenging queries that tested the AI's understanding of intricate microbiological concepts, processes, and terminologies. I carefully graded the responses for scientific accuracy, logical coherence, and depth of explanation, providing detailed feedback to enhance the AI's ability to handle advanced microbiological topics. My work ensured the system's capacity to deliver reliable and informed answers, particularly in high-stakes scenarios requiring precision in the life sciences domain.

In this project, I evaluated an AI system's performance in answering complex and objective microbiological questions. This involved designing challenging queries that tested the AI's understanding of intricate microbiological concepts, processes, and terminologies. I carefully graded the responses for scientific accuracy, logical coherence, and depth of explanation, providing detailed feedback to enhance the AI's ability to handle advanced microbiological topics. My work ensured the system's capacity to deliver reliable and informed answers, particularly in high-stakes scenarios requiring precision in the life sciences domain.

2024 - 2024

Dolphin Multimodal- Image

Scale AIImageQuestion AnsweringEmotion Recognition

The "Image-to-text" project involved evaluating the model's ability to interpret visual content and provide accurate and relevant responses. In this role, I uploaded images related to a specific topic (I.e. Microbiology), posed questions related to the content or details of the images, and assessed the AI's responses for precision and contextual alignment. This work required a strong understanding of both the visual elements and the nuances of effective AI response evaluation, ensuring the model's ability to process and respond accurately to visual data.

The "Image-to-text" project involved evaluating the model's ability to interpret visual content and provide accurate and relevant responses. In this role, I uploaded images related to a specific topic (I.e. Microbiology), posed questions related to the content or details of the images, and assessed the AI's responses for precision and contextual alignment. This work required a strong understanding of both the visual elements and the nuances of effective AI response evaluation, ensuring the model's ability to process and respond accurately to visual data.

2024 - 2024

Education

Work History