63 Generative AI jobs in Canada

Job Description

About Alexa Translations
Alexa Translations provides translation services in the legal, financial, and securities sectors by leveraging proprietary A.I. technology and a team of highly specialized linguistic experts. Unmatched in speed and quality, our machine translation engine is best-in-class and specifically trained for the French-Canadian market. If that wasn’t enough, our technology is backed by two decades of award-winning client service.

About the Role
We are looking for a Generative AI Engineer to develop our next-generation intelligent translation and translation-related service engine, using Generative AI (GenAI) and Large Language Model (LLM) technologies. You will report to the team lead on GenAI, develop and implement state-of-the-art algorithms by fast prototyping, and collaborate with the software team to deploy models. We expect our Generative AI Engineer to stay current with the technological cutting edge and build applications of LLM and GenAI to machine translation with best industry practices, as well as having solid background and hands-on experience with deep learning, machine learning, natural language processing, and big data.

Responsibilities

• Research and implement state-of-the-art LLM techniques including continued pre-training, instruction fine-tuning, preference alignment, and LLM deployment while also focusing on prompt engineering and GenAI more broadly.
• Work closely with machine learning engineers and data scientists to design, build, and test models.
• Contribute to technological innovations by staying current to the cutting-edge achievements of GenAI and LLM from industry and academia.
• Develop efficient and scalable algorithms for training and inference of generative models, leveraging deep learning frameworks such as TensorFlow or PyTorch and optimizing performance on diverse hardware platforms.
• Train and evaluate generative models using appropriate metrics and benchmarks, fine-tuning model parameters, architectures, and hyperparameters to optimize performance, stability, and generalization.
• Work closely with software and DevOps engineers to deploy GenAI models.
• Document code, algorithms, and experimental results, following best practices for reproducibility, version control, and software engineering, and contribute to internal knowledge sharing and continuous improvement initiatives.
   
• Requirements
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
• 1+ years of industry experience developing GenAI and LLM applications is preferred.
• 2+ years of professional experience as a software engineer is required.
• Proficiency in Python programming and software development practices, with experience in building and maintaining scalable, production-grade software systems.
• Working knowledge and project-based record of all of the following: prompt tuning, RAG, ICL.
• Working knowledge and project-based record of at least one of the following is a plus: continued pre-training, instruction fine-tuning, Agent.
• Strong problem-solving skills, attention to detail, and the ability to work independently and collaboratively in a fast-paced environment.
• Hands-on experience with Huggingface APIs or Amazon Bedrock.
• Expert skills of Python, including PyTorch, TensorFlow, Pandas, etc.
• Experience with cloud platforms like AWS, GCP, or Azure
• Self-driven, self-motivated with excellent time management skills
• Excellent communication skills, with the ability to convey complex technical concepts clearly and effectively to both technical and non-technical stakeholders.
• Familiarity with GPU programming and optimization techniques for accelerating deep learning computations.
• Ability to adapt to shifting priorities without compromising deadlines and momentum.
• Prior experience in generative AI research, projects, or internships, with contributions to open-source projects or publications in relevant conferences or journals.

Powered by JazzHR

Bc3vWesK9R

Job Description

About Alexa Translations
Alexa Translations provides translation services in the legal, financial, and securities sectors by leveraging proprietary A.I. technology and a team of highly specialized linguistic experts. Unmatched in speed and quality, our machine translation engine is best-in-class and specifically trained for the French-Canadian market. If that wasn’t enough, our technology is backed by two decades of award-winning client service.

About the Role
We are looking for a Generative AI Engineer to develop our next-generation intelligent translation and translation-related service engine, using Generative AI (GenAI) and Large Language Model (LLM) technologies. You will report to the team lead on GenAI, develop and implement state-of-the-art algorithms by fast prototyping, and collaborate with the software team to deploy models. We expect our Generative AI Engineer to stay current with the technological cutting edge and build applications of LLM and GenAI to machine translation with best industry practices, as well as having solid background and hands-on experience with deep learning, machine learning, natural language processing, and big data.

Responsibilities

• Research and implement state-of-the-art LLM techniques including continued pre-training, instruction fine-tuning, preference alignment, and LLM deployment while also focusing on prompt engineering and GenAI more broadly.
• Work closely with machine learning engineers and data scientists to design, build, and test models.
• Contribute to technological innovations by staying current to the cutting-edge achievements of GenAI and LLM from industry and academia.
• Develop efficient and scalable algorithms for training and inference of generative models, leveraging deep learning frameworks such as TensorFlow or PyTorch and optimizing performance on diverse hardware platforms.
• Train and evaluate generative models using appropriate metrics and benchmarks, fine-tuning model parameters, architectures, and hyperparameters to optimize performance, stability, and generalization.
• Work closely with software and DevOps engineers to deploy GenAI models.
• Document code, algorithms, and experimental results, following best practices for reproducibility, version control, and software engineering, and contribute to internal knowledge sharing and continuous improvement initiatives.
   
• Requirements
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
• 1+ years of industry experience developing GenAI and LLM applications is preferred.
• 2+ years of professional experience as a software engineer is required.
• Proficiency in Python programming and software development practices, with experience in building and maintaining scalable, production-grade software systems.
• Working knowledge and project-based record of all of the following: prompt tuning, RAG, ICL.
• Working knowledge and project-based record of at least one of the following is a plus: continued pre-training, instruction fine-tuning, Agent.
• Strong problem-solving skills, attention to detail, and the ability to work independently and collaboratively in a fast-paced environment.
• Hands-on experience with Huggingface APIs or Amazon Bedrock.
• Expert skills of Python, including PyTorch, TensorFlow, Pandas, etc.
• Experience with cloud platforms like AWS, GCP, or Azure
• Self-driven, self-motivated with excellent time management skills
• Excellent communication skills, with the ability to convey complex technical concepts clearly and effectively to both technical and non-technical stakeholders.
• Familiarity with GPU programming and optimization techniques for accelerating deep learning computations.
• Ability to adapt to shifting priorities without compromising deadlines and momentum.
• Prior experience in generative AI research, projects, or internships, with contributions to open-source projects or publications in relevant conferences or journals.

Powered by JazzHR

j4ZaZ1lRQ6

Job Description

Data Scientist - Generative AI

100% Remote, Canada (covering EST/PST timezones)
Experience: 4+ Years

Role Summary: NearSource is looking for a Data Scientist - Generative AI with expertise in advanced machine learning, large language models, and scalable data solutions. The selected candidate will design and deploy AI-driven systems for enterprise-grade platforms, collaborating with global teams to deliver impactful innovation.

Key Responsibilities

• Design and implement scalable machine learning models with a focus on Generative AI and LLMs
• Architect and optimize data pipelines leveraging big data platforms such as Hadoop, Spark, and Hive
• Develop high-quality, well-documented, and production-ready code in Python and related technologies
• Drive experimentation using advanced ML techniques, including classification, clustering, optimization, and dimensionality reduction
• Integrate AI/ML solutions with relational and NoSQL databases for enterprise-scale systems
• Apply data science toolkits (pandas, Jupyter, scikit-learn, TensorFlow) to deliver actionable insights
• Collaborate with cross-functional engineering teams to design and enhance recommender systems and AI-driven protocols
• Leverage cloud computing platforms (AWS, SageMaker) for model training, deployment, and scaling
• Communicate technical findings effectively to both technical and non-technical stakeholders

Must-Have Skills

• MS/PhD in Mathematics, Statistics, Physical Sciences, Computer Science, or related fields
• 4+ years of hands-on experience in designing and deploying ML-based solutions
• Strong proficiency with Python and SQL
• Experience with relational and NoSQL databases
• Expertise in big data platforms (Hadoop, Spark, Hive)
• Proficiency with ML frameworks and toolkits (pandas, Jupyter, scikit-learn, TensorFlow)
• Experience with Generative AI, LLMs (RAG, NLP), and context protocol design
• Ability to write scalable, production-grade, and well-documented code
• Familiarity with AWS cloud services, including SageMaker
• Strong communication skills for technical and non-technical presentations

Nice-to-Have Skills

• Familiarity with MLOps practices for optimizing ML workflows in production
• Experience designing recommender systems for enterprise applications
• Exposure to distributed systems and advanced AI model optimization techniques
• Experience leading research projects with publications in AI/ML domains
• Knowledge of containerization and orchestration (Docker, Kubernetes)

Apply now, or share your resume with salary expectations at . Thank you for considering a career with us! Once you submit your application, our Talent Acquisition team will review your resume thoroughly. If there's a strong match, we'll reach out to discuss your experience, role details, benefits, compensation, and next steps. While we strive for transparency, we may not be able to respond to every applicant due to high volume, but we genuinely appreciate your time and interest.

About NearSource: NearSource Technologies is a trusted partner for future-ready software consulting, enabling Fortune 500 enterprises to accelerate digital transformation. Our global engineering teams build and deploy impactful technology for some of the world's most admired brands, working directly on long-term client initiatives.

Equal Opportunity Statement: NearSource is an equal opportunity employer committed to fostering an inclusive and respectful environment. We celebrate diversity and do not discriminate based on race, gender, religion, sexual orientation, age, disability, or background. Innovation thrives when everyone feels empowered to contribute.

Data Scientist Generative AI

100% Remote, Canada (covering EST/PST timezones)
Experience: 4+ Years

Role Summary: NearSource is looking for a Data Scientist - Generative AI with expertise in advanced machine learning, large language models, and scalable data solutions. The selected candidate will design and deploy AI-driven systems for enterprise-grade platforms, collaborating with global teams to deliver impactful innovation.

Key Responsibilities

• Design and implement scalable machine learning models with a focus on Generative AI and LLMs
• Architect and optimize data pipelines leveraging big data platforms such as Hadoop, Spark, and Hive
• Develop high-quality, well-documented, and production-ready code in Python and related technologies
• Drive experimentation using advanced ML techniques, including classification, clustering, optimization, and dimensionality reduction
• Integrate AI/ML solutions with relational and NoSQL databases for enterprise-scale systems
• Apply data science toolkits (pandas, Jupyter, scikit-learn, TensorFlow) to deliver actionable insights
• Collaborate with cross-functional engineering teams to design and enhance recommender systems and AI-driven protocols
• Leverage cloud computing platforms (AWS, SageMaker) for model training, deployment, and scaling
• Communicate technical findings effectively to both technical and non-technical stakeholders

Must-Have Skills

• MS/PhD in Mathematics, Statistics, Physical Sciences, Computer Science, or related fields
• 4+ years of hands-on experience in designing and deploying ML-based solutions
• Strong proficiency with Python and SQL
• Experience with relational and NoSQL databases
• Expertise in big data platforms (Hadoop, Spark, Hive)
• Proficiency with ML frameworks and toolkits (pandas, Jupyter, scikit-learn, TensorFlow)
• Experience with Generative AI, LLMs (RAG, NLP), and context protocol design
• Ability to write scalable, production-grade, and well-documented code
• Familiarity with AWS cloud services, including SageMaker
• Strong communication skills for technical and non-technical presentations

Nice-to-Have Skills

• Familiarity with MLOps practices for optimizing ML workflows in production
• Experience designing recommender systems for enterprise applications
• Exposure to distributed systems and advanced AI model optimization techniques
• Experience leading research projects with publications in AI/ML domains
• Knowledge of containerization and orchestration (Docker, Kubernetes)

Apply now, or share your resume with salary expectations at .
Thank you for considering a career with us! Once you submit your application, our Talent Acquisition team will review your resume thoroughly. If there's a strong match, we'll reach out to discuss your experience, role details, benefits, compensation, and next steps. While we strive for transparency, we may not be able to respond to every applicant due to high volume, but we genuinely appreciate your time and interest.

About NearSource: NearSource Technologies is a trusted partner for future-ready software consulting, enabling Fortune 500 enterprises to accelerate digital transformation. Our global engineering teams build and deploy impactful technology for some of the world's most admired brands, working directly on long-term client initiatives.

Equal Opportunity Statement: NearSource is an equal opportunity employer committed to fostering an inclusive and respectful environment. We celebrate diversity and do not discriminate based on race, gender, religion, sexual orientation, age, disability, or background. Innovation thrives when everyone feels empowered to contribute.

Job Title: Generative AI Associate (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 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 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.