Innodata Inc.

Software Engineer

• Architect, design, and contribute code to mission-critical platform products including annotation platforms and agentic evaluation & monitoring systems
• Conduct thorough code reviews and establish engineering best practices across the team
• Make key technical decisions on platform architecture, technology stack, and system scalability
• Ensure platform reliability, security, and performance standards meet enterprise customer requirements
• Build, lead, and mentor a high-performing engineering team of 8-15 engineers
• Provide technical guidance and career development for engineers at all levels
• Establish engineering processes, standards, and culture that promote innovation and quality
• Recruit top engineering talent and scale the team to meet growing platform demands
• Partner closely with Data Science teams to understand AI model requirements and integrate ML workflows into platform architecture
• Collaborate with Product teams to translate product vision into technical roadmaps and deliverable features
• Work with Project Managers to ensure accurate project scoping, timeline estimation, and delivery commitments
• Coordinate with Customer Success and Sales teams to understand customer needs and technical requirements
• Communicate technical progress, challenges, and opportunities to executive leadership
• Manage expectations across internal stakeholders and external customers
• Present technical roadmaps and platform capabilities to both technical and non-technical audiences
• Balance competing priorities and resource allocation across multiple platform initiatives
• Drive platform strategy aligned with company goals and market opportunities
• Stay current with AI/ML infrastructure trends and emerging technologies
• Evaluate and implement new tools, frameworks, and methodologies to improve development efficiency
• Ensure platforms are designed for multi-tenancy, scalability, and extensibility

Director of Engineering - Platform Products

Artificial Intelligence

• 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 (e.g., 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.

Generative AI Specialist – Humanities, English and Polish

Generative AI Specialist – Humanities, English and Greek

• Evaluation: Rating/assessing AI model outputs through evaluative questions.
• Annotation Labeling: Labeling elements of content rather than whole content.
• Classification: Assigning predefined categories or labels.
• Content Quality: Evaluating perceived quality/appropriateness of content.
• Content Understanding: Generating labels to advance concept understanding.
• Data Augmentation: Creating additional training data via transformations.
• Grading: Reviewing data to identify product feature behavior per guidelines.
• Identification Labeling: Labeling outputs to identify specific content types.
• Preference Ranking: Ordering or ranking items by criteria.
• Prompt Generation: Creating prompts for language models.
• Relevance Evaluation: Evaluating content relevance on a scale.
• Response Generation: Generating responses to prompts using language models.
• Response Rewrite: Rewriting text to preserve meaning and improve clarity.
• Response Summarization: Producing concise summaries of longer text.
• Similarity Evaluation: Comparing content for determinations.
• Transcription: Converting spoken language/audio to text.
• Translation: Converting text/spoken language between languages.
• Data Collection: Gathering data for training, evaluating, or fine-tuning AI models.

Generative AI Specialist – Humanities, English and Danish

Job Title: Generative AI Specialist - Humanities (English and Norwegian)
Location: Fully Remote within the U.S. (excluding Alaska, California, Colorado, Nevada, Puerto Rico or Nebraska)
Employment Type: Flexible Part-Time Role (part-time, up to 25 hours weekly)
Core tasks include: Evaluation; Annotation Labeling; Classification; Content Quality; Content Understanding; Data Augmentation; Grading; Identification Labeling; Preference Ranking; Prompt Generation; Relevance Evaluation; Response Generation; Response Rewrite; Response Summarization; Similarity Evaluation; Transcription; Translation; Data Collection.
Projects typically range from 2 to 6 hours per day, depending on the need.

Generative AI Specialist – Humanities, English and Norwegian

• Evaluation: rating/assessing the performance of AI models or algorithms based on their output or behavior
• Annotation Labeling: labeling elements of a piece of content
• Classification: assigning predefined categories or labels to items
• Content Quality: evaluating perceived quality and/or appropriateness of content
• Content Understanding: generating labels to advance understanding of a concept
• Data Augmentation: creation of additional training data by applying transformations
• Grading: reviewing data and identifying whether a product feature works as intended
• Identification Labeling: labeling model outputs (e.g., clickbait, branded content)
• Preference Ranking: ordering or ranking items based on preferences
• Prompt Generation: creating prompts or questions for language models
• Relevance Evaluation: evaluating relevance of content
• Response Generation: generating responses to prompts or questions
• Response Rewrite: rewriting existing text to improve clarity or adherence to guidelines
• Response Summarization: producing concise summaries
• Similarity Evaluation: comparing content to drive determinations
• 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 for training

Generative AI Specialist – Humanities, English and Finnish

• Part-time, remote, flexible, project-specific role up to 25 hours weekly
• Tasks include evaluation, annotation labeling, classification, content quality assessment, content understanding, data augmentation, grading, identification labeling, preference ranking, prompt generation, relevance evaluation, response generation, response rewrite, response summarization, similarity evaluation, transcription, translation, data collection
• Contribute to training and evaluation of generative AI and LLMs as part of Subject Matter Expert (SME) on Demand program
• Work from within the U.S. excluding Alaska, California, Colorado, Nevada, Puerto Rico or Nebraska

Generative AI Specialist – Humanities, English and Hungarian

• Evaluation: Rating/assessing AI models or algorithms based on output or behavior through evaluative questions.
• Annotation Labeling: Labeling elements of content rather than the whole.
• Classification: Assigning predefined categories or labels.
• Content Quality: Evaluating perceived quality and/or appropriateness of content.
• Content Understanding: Generating labels to advance understanding of concepts or trends.
• Data Augmentation: Creating additional training data via transformations or generating new text.
• Grading: Reviewing data and identifying whether a product feature works as intended per guidelines.
• Identification Labeling: Labeling outputs to identify if content is or isn't something (e.g., clickbait, branded content).
• Preference Ranking: Ordering or ranking items based on preferences or criteria.
• Prompt Generation: Creating prompts or questions for language models.
• Relevance Evaluation: Evaluating content relevance on a scale.
• Response Generation: Generating responses to prompts using language models.
• Response Rewrite: Rewriting text to improve clarity or style.
• Response Summarization: Producing concise summaries of longer text.
• Similarity Evaluation: Comparing content to drive determinations.
• Transcription: Converting spoken language or audio to written text.
• Translation: Converting text or spoken language between languages.
• Data Collection: Gathering and compiling data for training or evaluating AI models.

Generative AI Specialist – Humanities, English and Swedish

Recruitment

• Manage the full recruitment lifecycle from intake to offer for roles across departments.
• Partner with hiring managers to understand staffing needs, role requirements, and ideal candidate profiles.
• Source passive and active candidates using LinkedIn, job boards, referrals, and other creative methods.
• Build and maintain a strong pipeline of qualified candidates across multiple functions.
• Schedule and coordinate interviews, manage candidate communications, and ensure a smooth candidate experience.
• Track and report recruitment metrics such as time-to-fill, pipeline health, and source effectiveness using advanced Excel functions (pivot tables, VLOOKUP/XLOOKUP, conditional formatting, formulas, etc.).
• Create custom Excel dashboards and visual reports to support data-driven hiring decisions.
• Maintain accurate and up-to-date data in the ATS and Excel trackers.
• Stay current on talent market trends and recommend innovative sourcing strategies.