Innodata

• Receive single-turn user-model interactions and produce three rewrites of the model output per interaction. • Apply project guidelines, rubric standards, and AP style requirements consistently across all submissions. • Shift between writing contexts — marketing copy, personal essays, business communication, pitch writing, and more — within a single workflow. • Submit work through a structured Excel-based production template on a batch schedule. • Participate in training and pass a certification assessment before entering production.

• Create basic design artifacts (mockups, flows, concepts) • Present and explain design decisions in team meetings • Collaborate with product and engineering on feasibility and UX tradeoffs • Iterate on designs based on feedback • Contribute to documentation and discussions

• Serve as the primary creator of sales collateral, including pitch decks, one-pagers, solution briefs, and proposal assets • Produce a steady cadence of content to support active sales opportunities and campaigns • Translate complex AI and data solutions into clear, buyer-friendly messaging • Tailor content for different industries, use cases, and stages of the sales cycle • Collaborate closely with Marketing, Sales, and Solutions teams to gather inputs and refine messaging • Ensure all materials align with Innodata’s messaging, positioning, and brand standards • Create and maintain templates and repeatable frameworks for decks and one-pagers • Continuously improve content based on feedback and performance • Maintain and organize a centralized library of sales materials.

• Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. • Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole. • Classification: Assigning predefined categories or labels to items. • Content Quality: Evaluating the perceived quality and/or appropriateness of content. • Content Understanding: Generating labels to advance understanding of a concept, trend etc. • Data Augmentation: Creation of additional training data for machine learning models by applying transformations to the original data. • Grading: Reviewing data and identifying whether or not a product feature works as intended. • Identification Labeling: Labeling model outputs to identify if a piece of content is or isn't something. • Preference Ranking: Ordering or ranking items based on a set of preferences or criteria. • Prompt Generation: Creating prompts or questions that will be used to generate responses from a language model or other AI system. • Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale. • Response Generation: Generating responses to prompts or questions using a language model or other AI system. • Response Rewrite: Rewriting existing text while preserving the original meaning. • Response Summarization: Producing concise summaries of longer pieces of text or data. • Similarity Evaluation: Projects where content is compared in order to drive a determination. • Transcription: Converting spoken language or audio content into written text. • Translation: Converting text or spoken language from one language to another. • Data Collection: Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models.

• Sales Enablement for AI Solutions by translating AI capabilities into sales narratives and customer-ready materials • Building repeatable, high-conversion sales assets • Acting as a bridge between practices and sales to align offerings • Supporting priority opportunities by structuring deal narratives and coordinating inputs across teams • Developing targeted sales plays by industry, use case, and buyer persona • Capturing feedback from sales and SDR teams to refine positioning and assets

• Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. • Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole. • Classification: Assigning predefined categories or labels to items. • Content Quality: Evaluating the perceived quality and/or appropriateness of content • Content Understanding: Generating labels to advance understanding of a concept, trend etc. • 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. • Grading: Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. • 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. • Preference Ranking: Ordering or ranking items based on a set of preferences or criteria. • Prompt Generation: Creating prompts or questions that will be used to generate responses from a language model or other AI system. • Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). • Response Generation: Generating responses to prompts or questions using a language model or other AI system. • Response Rewrite: Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. • Response Summarization: Producing concise summaries of longer pieces of text or data. • Similarity Evaluation: Projects where content is compared in order to drive a determination. • Transcription: Converting spoken language or audio content into written text. • Translation: Converting text or spoken language from one language to another. • 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.

• Design Physical AI problem formulations • Prototype perception, world‑model, and action‑representation pipelines • Use simulation and synthetic environments to generate datasets • Work directly with customers’ robotics and ML teams • Lead technical discovery and pre‑sales pilots • Collaborate with internal data‑collection and platform teams • Develop reusable playbooks, reference architectures, and demos

• Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. • Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole. • Classification: Assigning predefined categories or labels to items. • Content Quality: Evaluating the perceived quality and/or appropriateness of content. • Data Augmentation: Creation of additional training data for machine learning models by applying transformations to the original data. • Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale. • Response Generation: Generating responses to prompts or questions using a language model or other AI system.

• Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. • Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole. • Classification: Assigning predefined categories or labels to items. • Content Quality: Evaluating the perceived quality and/or appropriateness of content. • Content Understanding: Generating labels to advance understanding of a concept, trend etc. • Data Augmentation: Creation of additional training data for machine learning models by applying transformations to the original data. • Grading: Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. • Identification Labeling: Labeling model outputs to identify if a piece of content is or isn't something. • Preference Ranking: Ordering or ranking items based on a set of preferences or criteria. • Prompt Generation: Creating prompts or questions that will be used to generate responses from a language model or other AI system. • Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale. • Response Generation: Generating responses to prompts or questions using a language model or other AI system. • Response Rewrite: Rewriting existing text while preserving the original meaning. • Response Summarization: Producing concise summaries of longer pieces of text or data. • Similarity Evaluation: Projects where content is compared in order to drive a determination. • Transcription: Converting spoken language or audio content into written text. • Translation: Converting text or spoken language from one language to another. • Data Collection: Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models.

• Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. • Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole. • Classification: Assigning predefined categories or labels to items. • Content Quality: Evaluating the perceived quality and/or appropriateness of content • Data Augmentation: Creation of additional training data for machine learning models by applying transformations to the original data