Prepare for and perform two unscripted roleplayed customer service calls, following the provided scripts, policies, and vocal performance guidelines. Record and upload both sides of each call, then submit structured evaluations with scores, error tags, and timestamped notes within 24 hours.
The role involves evaluating and labeling AI model outputs to improve language and reasoning capabilities. Responsibilities include creating training data, ranking content, and performing data transformations to support machine learning development.
The role involves evaluating and labeling AI model outputs, creating training data, and performing tasks like summarization and translation to improve LLM performance. You will also be responsible for generating prompts and assessing the relevance and quality of content based on project guidelines.
The role involves assessing and labeling AI model outputs, creating training data, and generating prompts to improve large language models. You will also be responsible for rewriting text, summarizing content, and evaluating the relevance and quality of AI-generated responses.
The Business Development Representative will identify and qualify new business opportunities within the financial services sector by targeting AI and data transformation initiatives. This role involves conducting outbound prospecting, managing account research, and coordinating meetings between prospective clients and internal subject matter experts.
The Business Development Representative will identify and qualify new business opportunities within the Physical AI ecosystem, including robotics and autonomous systems. They will manage outbound prospecting, conduct discovery calls, and coordinate the transition of qualified leads into commercial engagements.
The Business Development Representative will identify and engage new business opportunities across the healthcare and life sciences sectors to drive AI adoption. This role involves conducting outbound prospecting, managing discovery calls, and coordinating with internal experts to transition prospects into commercial engagements.
The specialist will conduct real-time, voice-based conversations with AI models to evaluate their performance based on defined quality criteria. They are responsible for comparing model responses, identifying error clusters, and writing detailed rationales for their performance assessments.
The Director/Client Partner will own the account strategy, revenue growth, and pipeline development for a portfolio of strategic technology accounts. They will build executive-level relationships and collaborate with internal teams to expand Enterprise AI engagements and drive commercial success.
The Director/Client Partner will own account strategy, business development, and revenue growth for strategic Information Services customers. They will collaborate with internal AI and engineering teams to design and implement Enterprise AI solutions that drive measurable business outcomes.
The Director/Client Partner will lead the strategic growth of the Google account by expanding Innodata's footprint across AI, cloud, and data engineering services. This role involves managing executive relationships, driving revenue performance, and collaborating with internal teams to deliver complex AI transformation programs.
Design and implement effective prompt strategies to automate and optimize data annotation and human evaluation workflows using LLMs. Collaborate with cross-functional teams to translate business requirements into scalable AI-driven solutions while ensuring high standards of quality and reliability.
Review and correct AI-generated building-code compliance determinations on architectural permit applications. Ensure accuracy of pass/fail verdicts and citations by applying expert knowledge of building codes.
The Sales Development Representative will identify and qualify prospective clients through research and multi-channel outreach. They will manage the sales pipeline in Salesforce and collaborate with the Sales Director to ensure a seamless lead handoff.
You will define and validate how audio data is structured and evaluated for speech models, ensuring high-quality benchmarks for ASR, TTS, and conversational AI. You will also partner with engineering and linguistics teams to translate model objectives into concrete data specifications and run experiments to prove the impact of data decisions.
You will define and validate data specifications and evaluation methodologies for video and multimodal AI models. This includes running experiments to prove data decisions and publishing research to advance the field.
The Research Scientist will define and validate data specifications for robot foundation models, including modalities, action representations, and evaluation criteria. They will also collaborate with internal teams to design collection pipelines and conduct experiments to improve model performance through data-quality ablations.
The Principal Speech Data Linguist will define and own linguistic standards, style guides, and quality frameworks for high-volume transcription and segmentation workflows. They will also design human-in-the-loop processes and partner with research scientists to ensure data quality meets the requirements of advanced AI models.
The specialist will conduct real-time voice conversations with AI models to evaluate their performance based on defined quality criteria. They are responsible for documenting observations, identifying error clusters, and providing a detailed rationale for model preference.
The Technical Training & Quality Manager will lead technical training, capability development, and quality assurance for AI data programs. They will collaborate with cross-functional teams to ensure data annotation and model evaluation workflows meet high-quality standards.
The Research Data Scientist will conduct independent and collaborative research in Generative AI, LLMs, and NLP to develop evaluation frameworks and analytical prototypes. They will also translate complex research findings into actionable AI/ML solutions while collaborating with cross-functional teams to improve model performance.
The Technical Delivery Manager will own end-to-end program management for AI/ML engagements, serving as the primary point of contact for clients and internal stakeholders. They are responsible for orchestrating delivery roadmaps, managing risks, and ensuring alignment between client expectations and technical execution.
The evaluator will review and score AI-generated customer interactions across various dimensions including faithfulness, tone, and compliance. They will also benchmark customer queries against authoritative business sources to ensure accuracy and quality.
The evaluator will score AI-generated customer interactions for accuracy, tone, and compliance against established rubrics. They will also perform intent and fact verification by benchmarking queries against authoritative business knowledge bases.
The role involves leading end-to-end analytics initiatives, architecting production-grade SQL pipelines, and setting standards for data models and dashboards. You will serve as a senior technical leader and thought partner to Data Science and Product teams to solve complex monetization and revenue data problems.
The role involves leading end-to-end analytics initiatives, setting standards for data models and pipelines, and partnering with Data Science and Product teams to solve complex monetization problems. You will also be responsible for architecting production-grade SQL pipelines and enabling self-serve analytics through high-quality dashboards.
The role involves evaluating and labeling AI model outputs to improve language and reasoning capabilities. Responsibilities include creating training data, ranking content, and performing tasks like summarization and translation to support large language model development.
The role involves wearing a head-mounted camera to record everyday household tasks to help train robotics and AI systems. Responsibilities include following scripted instructions, maintaining consistent procedures, and reporting technical issues with equipment.
The role involves evaluating and labeling AI model outputs to improve language and reasoning capabilities. Responsibilities include creating training data, generating prompts, and performing quality assessments on various digital content types.
The role involves rating AI model performance, labeling content, and creating prompts to improve LLM reasoning and language capabilities. Responsibilities also include generating training data, rewriting text for clarity, and compiling digital content for model fine-tuning.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
The role involves rating, labeling, and ranking AI model outputs to improve the reasoning and language capabilities of LLMs. Responsibilities include creating prompts, generating training data, and rewriting text for clarity and adherence to guidelines.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for machine learning.
The specialist will rate AI model performance, label content, and create training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
The role involves rating AI model performance, labeling content, and creating prompts to train large language models. Responsibilities also include generating training data through text transformation, summarization, and translation.
The role involves rating AI model performance, labeling data, and creating prompts to improve the reasoning and language capabilities of LLMs. Responsibilities also include generating training data through text transformation, summarization, and translation.
The role involves rating AI model performance, labeling content, and creating training data through prompts and text transformations. Responsibilities also include rewriting text for clarity and converting audio or language content to improve LLM reasoning.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
The role involves rating AI model performance, labeling content, and creating prompts to improve LLM reasoning and language capabilities. Responsibilities also include generating training data through text transformation, summarization, and translation.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language skills. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
The role involves rating AI model performance, labeling content, and creating training data through prompts and text transformations. Responsibilities also include rewriting text for clarity and converting audio or language to improve LLM reasoning and quality.
