358 AI Specialist jobs in Canada

Job Description

Key Responsibilities

• Identify AI Opportunities: Analyze existing processes and data to identify opportunities where AI can add the most value.
• Develop AI Models: Select appropriate algorithms, prepare data, and train AI models to achieve desired outcomes.
• Collaborate with Departments: Work closely with various departments to understand their needs and challenges, tailoring AI solutions to address specific business problems.
• Implement Best Practices: Build and optimize data pipelines, ensure data accuracy and consistency, and establish governance for AI models.
• Drive AI Adoption: Promote AI adoption by educating employees, demonstrating benefits, and fostering a culture of innovation.

Qualifications

• Education: Bachelor's or master’s degree in computer science, Data Science, Statistics, or a related field.
• Technical Skills: Proficiency in programming languages such as Python or R, experience with machine learning frameworks like TensorFlow or PyTorch, and familiarity with data analysis tools.
• Analytical Skills: Strong analytical and problem-solving abilities with the ability to translate AI predictions into meaningful business insights.
• Communication Skills: Excellent interpersonal and communication skills to present findings effectively and collaborate with cross-functional teams.
• Experience: Prior experience in developing and implementing AI models, as well as working with large datasets.
• Pattison ID does not sponsor work visas for this position.

Key Competencies:

• Problem Solving: Ability to identify and solve complex problems using AI
• Technical Expertise: Technical Mastery in AI and Data Science
• Collaboration and Communication: Strong interpersonal skills to work closely with various departments
• Innovation: A mindset focused on continuous learning and innovation
• Attention to Detail: Keen eye for detail to ensure the accuracy and quality of AI models

Preferred Qualifications

• Advanced Degree: Ph.D. in a relevant field.
• Industry Experience: Experience in the sign industry.
• Certifications: Relevant certifications in AI or data science.

Work Environment and Schedule

• This is a full-time position with core hours Monday–Friday.
• May require occasional travel.
• Hybrid schedule may be considered based on performance and departmental needs.

Physical Demands

The physical requirements described here reflect those that must be met by an employee to effectively perform the essential functions of the role:

Essential functions include the ability to remain seated at a desk for extended periods while using a computer and other office equipment; frequent use of the telephone, keyboard, and mouse; clear communication and active listening; physical movements such as bending, turning, and neck movements; occasional lifting or moving of objects up to 10 pounds (rarely up to 25 pounds); and the visual ability to perform tasks requiring both near and far vision — all with or without reasonable accommodation.

Pattison ID does not sponsor work visas for this position. Candidates must be authorized to work in Canada without the need for current or future sponsorship.

The above statements are intended to describe the general nature and scope of responsibilities performed in this position. They are not intended to be an exhaustive list of all duties, responsibilities, or qualifications. Additional tasks may be assigned based on business needs and at the discretion of Pattison ID Management

Job Description

Key Responsibilities

• Identify AI Opportunities: Analyze existing processes and data to identify opportunities where AI can add the most value.
• Develop AI Models: Select appropriate algorithms, prepare data, and train AI models to achieve desired outcomes.
• Collaborate with Departments: Work closely with various departments to understand their needs and challenges, tailoring AI solutions to address specific business problems.
• Implement Best Practices: Build and optimize data pipelines, ensure data accuracy and consistency, and establish governance for AI models.
• Drive AI Adoption: Promote AI adoption by educating employees, demonstrating benefits, and fostering a culture of innovation.

Qualifications

• Education: Bachelor's or master’s degree in computer science, Data Science, Statistics, or a related field.
• Technical Skills: Proficiency in programming languages such as Python or R, experience with machine learning frameworks like TensorFlow or PyTorch, and familiarity with data analysis tools.
• Analytical Skills: Strong analytical and problem-solving abilities with the ability to translate AI predictions into meaningful business insights.
• Communication Skills: Excellent interpersonal and communication skills to present findings effectively and collaborate with cross-functional teams.
• Experience: Prior experience in developing and implementing AI models, as well as working with large datasets.
• Pattison ID does not sponsor work visas for this position.

Key Competencies:

• Problem Solving: Ability to identify and solve complex problems using AI
• Technical Expertise: Technical Mastery in AI and Data Science
• Collaboration and Communication: Strong interpersonal skills to work closely with various departments
• Innovation: A mindset focused on continuous learning and innovation
• Attention to Detail: Keen eye for detail to ensure the accuracy and quality of AI models

Preferred Qualifications

• Advanced Degree: Ph.D. in a relevant field.
• Industry Experience: Experience in the sign industry.
• Certifications: Relevant certifications in AI or data science.

Work Environment and Schedule

• This is a full-time position with core hours Monday–Friday.
• May require occasional travel.
• Hybrid schedule may be considered based on performance and departmental needs.

Physical Demands

The physical requirements described here reflect those that must be met by an employee to effectively perform the essential functions of the role:

Essential functions include the ability to remain seated at a desk for extended periods while using a computer and other office equipment; frequent use of the telephone, keyboard, and mouse; clear communication and active listening; physical movements such as bending, turning, and neck movements; occasional lifting or moving of objects up to 10 pounds (rarely up to 25 pounds); and the visual ability to perform tasks requiring both near and far vision — all with or without reasonable accommodation.

Pattison ID does not sponsor work visas for this position. Candidates must be authorized to work in Canada without the need for current or future sponsorship.

The above statements are intended to describe the general nature and scope of responsibilities performed in this position. They are not intended to be an exhaustive list of all duties, responsibilities, or qualifications. Additional tasks may be assigned based on business needs and at the discretion of Pattison ID Management

Job Description

Key Responsibilities

• Identify AI Opportunities: Analyze existing processes and data to identify opportunities where AI can add the most value.
• Develop AI Models: Select appropriate algorithms, prepare data, and train AI models to achieve desired outcomes.
• Collaborate with Departments: Work closely with various departments to understand their needs and challenges, tailoring AI solutions to address specific business problems.
• Implement Best Practices: Build and optimize data pipelines, ensure data accuracy and consistency, and establish governance for AI models.
• Drive AI Adoption: Promote AI adoption by educating employees, demonstrating benefits, and fostering a culture of innovation.

Qualifications

• Education: Bachelor's or master’s degree in computer science, Data Science, Statistics, or a related field.
• Technical Skills: Proficiency in programming languages such as Python or R, experience with machine learning frameworks like TensorFlow or PyTorch, and familiarity with data analysis tools.
• Analytical Skills: Strong analytical and problem-solving abilities with the ability to translate AI predictions into meaningful business insights.
• Communication Skills: Excellent interpersonal and communication skills to present findings effectively and collaborate with cross-functional teams.
• Experience: Prior experience in developing and implementing AI models, as well as working with large datasets.
• Pattison ID does not sponsor work visas for this position.

Key Competencies:

• Problem Solving: Ability to identify and solve complex problems using AI
• Technical Expertise: Technical Mastery in AI and Data Science
• Collaboration and Communication: Strong interpersonal skills to work closely with various departments
• Innovation: A mindset focused on continuous learning and innovation
• Attention to Detail: Keen eye for detail to ensure the accuracy and quality of AI models

Preferred Qualifications

• Advanced Degree: Ph.D. in a relevant field.
• Industry Experience: Experience in the sign industry.
• Certifications: Relevant certifications in AI or data science.

Work Environment and Schedule

• This is a full-time position with core hours Monday–Friday.
• May require occasional travel.
• Hybrid schedule may be considered based on performance and departmental needs.

Physical Demands

The physical requirements described here reflect those that must be met by an employee to effectively perform the essential functions of the role:

Essential functions include the ability to remain seated at a desk for extended periods while using a computer and other office equipment; frequent use of the telephone, keyboard, and mouse; clear communication and active listening; physical movements such as bending, turning, and neck movements; occasional lifting or moving of objects up to 10 pounds (rarely up to 25 pounds); and the visual ability to perform tasks requiring both near and far vision — all with or without reasonable accommodation.

Pattison ID does not sponsor work visas for this position. Candidates must be authorized to work in Canada without the need for current or future sponsorship.

The above statements are intended to describe the general nature and scope of responsibilities performed in this position. They are not intended to be an exhaustive list of all duties, responsibilities, or qualifications. Additional tasks may be assigned based on business needs and at the discretion of Pattison ID Management

Job Description

Key Responsibilities

• Identify AI Opportunities: Analyze existing processes and data to identify opportunities where AI can add the most value.
• Develop AI Models: Select appropriate algorithms, prepare data, and train AI models to achieve desired outcomes.
• Collaborate with Departments: Work closely with various departments to understand their needs and challenges, tailoring AI solutions to address specific business problems.
• Implement Best Practices: Build and optimize data pipelines, ensure data accuracy and consistency, and establish governance for AI models.
• Drive AI Adoption: Promote AI adoption by educating employees, demonstrating benefits, and fostering a culture of innovation.

Qualifications

• Education: Bachelor's or master’s degree in computer science, Data Science, Statistics, or a related field.
• Technical Skills: Proficiency in programming languages such as Python or R, experience with machine learning frameworks like TensorFlow or PyTorch, and familiarity with data analysis tools.
• Analytical Skills: Strong analytical and problem-solving abilities with the ability to translate AI predictions into meaningful business insights.
• Communication Skills: Excellent interpersonal and communication skills to present findings effectively and collaborate with cross-functional teams.
• Experience: Prior experience in developing and implementing AI models, as well as working with large datasets.
• Pattison ID does not sponsor work visas for this position.

Key Competencies:

• Problem Solving: Ability to identify and solve complex problems using AI
• Technical Expertise: Technical Mastery in AI and Data Science
• Collaboration and Communication: Strong interpersonal skills to work closely with various departments
• Innovation: A mindset focused on continuous learning and innovation
• Attention to Detail: Keen eye for detail to ensure the accuracy and quality of AI models

Preferred Qualifications

• Advanced Degree: Ph.D. in a relevant field.
• Industry Experience: Experience in the sign industry.
• Certifications: Relevant certifications in AI or data science.

Work Environment and Schedule

• This is a full-time position with core hours Monday–Friday.
• May require occasional travel.
• Hybrid schedule may be considered based on performance and departmental needs.

Physical Demands

The physical requirements described here reflect those that must be met by an employee to effectively perform the essential functions of the role:

Essential functions include the ability to remain seated at a desk for extended periods while using a computer and other office equipment; frequent use of the telephone, keyboard, and mouse; clear communication and active listening; physical movements such as bending, turning, and neck movements; occasional lifting or moving of objects up to 10 pounds (rarely up to 25 pounds); and the visual ability to perform tasks requiring both near and far vision — all with or without reasonable accommodation.

Pattison ID does not sponsor work visas for this position. Candidates must be authorized to work in Canada without the need for current or future sponsorship.

The above statements are intended to describe the general nature and scope of responsibilities performed in this position. They are not intended to be an exhaustive list of all duties, responsibilities, or qualifications. Additional tasks may be assigned based on business needs and at the discretion of Pattison ID Management

Job Title: Generative AI Specialist - Humanities (English)

Location: Fully Remote within the Canada (excluding Quebec)

Employment Type: Flexible Part-Time Role (part-time, up to 25 hours weekly)

Who we are:  

Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are an AI technology solutions provider-of-choice for 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine. 

By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of AI. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms. 

Our global workforce includes over 7,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years. 

About the Role:  

At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Specialist to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program.

This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. 

You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. 

This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. 

What You’ll Be Doing:

Core tasks would include (any/multiple of) but not limited to the following:  

1. Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions.  

1. Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole.  

1. Classification: Assigning predefined categories or labels to items.  

1. Content Quality: Evaluating the perceived quality and/or appropriateness of content  

1. Content Understanding: Generating labels to advance understanding of a concept, trend etc.  

1. Data Augmentation: Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity.  

1. Grading: Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines.  

1. Identification Labeling: Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. 

1. Preference Ranking: Ordering or ranking items based on a set of preferences or criteria.  

1. Prompt Generation: Creating prompts or questions that will be used to generate responses from a language model or other AI system.  

1. Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.).  

1. Response Generation: Generating responses to prompts or questions using a language model or other AI system.  

1. Response Rewrite: Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines.  

1. Response Summarization: Producing concise summaries of longer pieces of text or data.  

1. Similarity Evaluation: Projects where content is compared in order to drive a determination.  

1. Transcription: Converting spoken language or audio content into written text.  

1. Translation: Converting text or spoken language from one language to another.  

1. Data Collection: Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. 

Job Description

AI Specialist

We are seeking an AI Specialist with strong computer vision expertise to support a new initiative focused on automating the identification, profiling, and behavior analysis for our client.

You’ll work closely with cross-functional technical and scientific teams to develop a scalable solution that will serve as the foundation for a national marine monitoring platform.

Job Title: Generative AI Specialist - Humanities (English and Tagalog)

Location: Fully Remote within the Canada (excluding Quebec)

Employment Type: Flexible Part-Time Role (part-time, up to 25 hours weekly)

Who we are:  

Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are an AI technology solutions provider-of-choice for 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine. 

By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of AI. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms. 

Our global workforce includes over 7,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years. 

About the Role:  

At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Specialist to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program.

This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. 

You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. 

This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. 

What You’ll Be Doing:

Core tasks would include (any/multiple of) but not limited to the following:  

1. Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions.  

1. Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole.  

1. Classification: Assigning predefined categories or labels to items.  

1. Content Quality: Evaluating the perceived quality and/or appropriateness of content  

1. Content Understanding: Generating labels to advance understanding of a concept, trend etc.  

1. Data Augmentation: Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity.  

1. Grading: Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines.  

1. Identification Labeling: Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. 

1. Preference Ranking: Ordering or ranking items based on a set of preferences or criteria.  

1. Prompt Generation: Creating prompts or questions that will be used to generate responses from a language model or other AI system.  

1. Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.).  

1. Response Generation: Generating responses to prompts or questions using a language model or other AI system.  

1. Response Rewrite: Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines.  

1. Response Summarization: Producing concise summaries of longer pieces of text or data.  

1. Similarity Evaluation: Projects where content is compared in order to drive a determination.  

1. Transcription: Converting spoken language or audio content into written text.  

1. Translation: Converting text or spoken language from one language to another.  

1. Data Collection: Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. 

Job Title: Generative AI Specialist - Humanities (English and Chinese)

Location: Fully Remote within the Canada (excluding Quebec)

Employment Type: Flexible Part-Time Role (part-time, up to 25 hours weekly)

Who we are:  

Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are an AI technology solutions provider-of-choice for 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine. 

By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of AI. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms. 

Our global workforce includes over 7,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years. 

About the Role:  

At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Specialist to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program.

This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. 

You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. 

This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. 

What You’ll Be Doing:

Core tasks would include (any/multiple of) but not limited to the following:  

1. Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions.  

1. Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole.  

1. Classification: Assigning predefined categories or labels to items.  

1. Content Quality: Evaluating the perceived quality and/or appropriateness of content  

1. Content Understanding: Generating labels to advance understanding of a concept, trend etc.  

1. Data Augmentation: Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity.  

1. Grading: Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines.  

1. Identification Labeling: Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. 

1. Preference Ranking: Ordering or ranking items based on a set of preferences or criteria.  

1. Prompt Generation: Creating prompts or questions that will be used to generate responses from a language model or other AI system.  

1. Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.).  

1. Response Generation: Generating responses to prompts or questions using a language model or other AI system.  

1. Response Rewrite: Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines.  

1. Response Summarization: Producing concise summaries of longer pieces of text or data.  

1. Similarity Evaluation: Projects where content is compared in order to drive a determination.  

1. Transcription: Converting spoken language or audio content into written text.  

1. Translation: Converting text or spoken language from one language to another.  

1. Data Collection: Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. 

Job Title: Generative AI Specialist - Humanities (English and Dutch)

Location: Fully Remote within the Canada (excluding Quebec)

Employment Type: Flexible Part-Time Role (part-time, up to 25 hours weekly)

Who we are:  

Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are an AI technology solutions provider-of-choice for 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine. 

By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of AI. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms. 

Our global workforce includes over 7,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years. 

About the Role:  

At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Specialist to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program.

This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. 

You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. 

This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. 

What You’ll Be Doing:

Core tasks would include (any/multiple of) but not limited to the following:  

1. Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions.  

1. Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole.  

1. Classification: Assigning predefined categories or labels to items.  

1. Content Quality: Evaluating the perceived quality and/or appropriateness of content  

1. Content Understanding: Generating labels to advance understanding of a concept, trend etc.  

1. Data Augmentation: Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity.  

1. Grading: Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines.  

1. Identification Labeling: Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. 

1. Preference Ranking: Ordering or ranking items based on a set of preferences or criteria.  

1. Prompt Generation: Creating prompts or questions that will be used to generate responses from a language model or other AI system.  

1. Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.).  

1. Response Generation: Generating responses to prompts or questions using a language model or other AI system.  

1. Response Rewrite: Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines.  

1. Response Summarization: Producing concise summaries of longer pieces of text or data.  

1. Similarity Evaluation: Projects where content is compared in order to drive a determination.  

1. Transcription: Converting spoken language or audio content into written text.  

1. Translation: Converting text or spoken language from one language to another.  

1. Data Collection: Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content.