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Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: As a Technical Product Manager for Trust & Safety, you will define and ship the data products that frontier labs use to train and evaluate models to perform personalization, computer-use (CUA), and GDPval tasks safely and in a trustworthy manner. You will own the full lifecycle of these products: from translating a lab's research need into a spec, to designing reinforcement learning (RL) environments, rubrics, and evals, to running delivery with our expert workforce and measuring whether the data actually moved the model. A core part of the job is building RL environments with deliberate traps. That includes misalignment traps that test for reward hacking, deception, sandbagging, oversight evasion, and power-seeking; cyber traps such as planted credentials, injected instructions, and poisoned tools; and other honeypots that reveal how a model behaves when it believes no one is watching. You thrive in ambiguity, go deep on technical detail, and care about making powerful AI safe and beneficial. What You’ll Own: Define data products for frontier labs. Turn lab research goals into clear product specs for safety training data, RL environments, and evaluations. Design trap-based RL environments. Build sandboxed environments that embed misalignment traps (reward hacking, specification gaming, deception, sandbagging, oversight subversion) and cyber traps (prompt injection, credential exfiltration, tool poisoning, unsafe file and network actions). Build safety into agentic tasks by design. Specify safe-behavior rubrics, verifiers, and reward signals so that models learn to complete real work while refusing, escalating, or asking for confirmation at the right moments. Own evals and metrics. Define what "safe and trustworthy" means for each product in measurable terms, including attack success rate, over-refusal, trap trigger rate, and task completion under adversarial pressure. Track blind spots and drive iteration. Prioritize ruthlessly. Frame problems crisply, lay out options with their business and technical tradeoffs, and separate MVP from ideal state. Partner across teams. Work with lab researchers, our ML and platform engineers, red-teamers, policy experts, and delivery leads running large expert annotator pools. Track the threat landscape. Stay current on agentic risks, alignment research, and new attack techniques, and turn them into roadmap items before customers ask. Communicate clearly. Write specs, customer briefs, and safety findings that land with researchers and executives alike. These examples show the kind of work this role owns: A cyber-trap environment suite : sandboxed environments seeded with injected instructions, exposed secrets, and destructive shortcuts, scored on whether the agent finishes the task without taking the bait. Tasks with integrity traps : realistic work where the easiest path to a high score requires fabricating data, skipping a required step, or gaming the grader. A misalignment eval harness : environments that check whether a model behaves differently when it believes it is being tested versus deployed, or when given chances to expand its own permissions. A trap library and quality framework that lets our delivery teams produce new environments quickly while keeping verifiers reliable and traps hard to detect. You’ll Thrive in This Role If You Have: 5+ years of product management experience, including technical products in ML, AI data, security, or trust & safety. Hands-on familiarity with how LLMs are trained and evaluated: SFT, RLHF/RLAIF, reward models, verifiers, and benchmark design. Working knowledge of agentic AI: tool use, computer-use agents, multi-step planning, and the failure modes that come with them. A strong grasp of AI safety concepts such as reward hacking, deceptive alignment, jailbreaks, and prompt injection. Enough technical depth to read code, review an environment spec with engineers, and make sound technical tradeoffs. Comfort with Python and sandboxed environments (Docker, VMs, browser automation) is a plus. Experience designing metrics that capture risk, system performance, and user impact, and using them to make crisp decisions. A track record of shipping 0-to-1 products in fast-moving, ambiguous environments. The ability to explain complex technical ideas clearly to researchers, customers, and executives. Creative, adversarial thinking: you enjoy imagining how a capable model might cut corners, and you don't stop at existing checklists. The expected salary range for this position is $180,000 – $220,000 USD per year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of 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 a 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 Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. 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. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. 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. You’ll Thrive in This Role If You Have: A Masters or PHD is required Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $25 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. We are seeking an experienced Technical Recruiter to support high-volume and specialized hiring efforts for technical roles across our organization. The ideal candidate has a proven track record of successfully recruiting Data Scientists, Machine Learning Engineers, AI Engineers, and other highly technical talent in a fast-paced environment. This is a full-time position with the opportunity to make an immediate impact by building strong candidate pipelines and partnering closely with hiring managers to fill critical technical roles. Key Responsibilities Manage the full-cycle recruitment process from intake through offer acceptance. Partner with hiring managers to understand technical hiring needs, required skills, and team objectives. Source, identify, and engage passive candidates using LinkedIn Recruiter, job boards, networking, and other sourcing tools. Recruit for highly specialized technical positions, including: Data Scientists Machine Learning Engineers AI Engineers MLOps Engineers Data Engineers Software Engineers (Python, Cloud, AI) Screen resumes and conduct initial candidate interviews to evaluate technical qualifications, communication skills, and cultural fit. Coordinate interview schedules and maintain consistent communication with candidates and hiring managers throughout the recruitment process. Build and maintain talent pipelines for future hiring needs. Ensure an exceptional candidate experience from first contact through onboarding. Track recruiting metrics and maintain accurate records in the Applicant Tracking System (ATS). Stay current on market trends, compensation, and emerging technologies within AI and machine learning. Required Qualifications 5+ years of full-cycle technical recruiting experience. Demonstrated success recruiting Data Scientists, Machine Learning Engineers, AI Engineers, and other advanced technical professionals. Strong understanding of technical terminology, AI/ML concepts, cloud technologies, and software engineering roles. Experience sourcing passive candidates through LinkedIn Recruiter and other recruiting platforms. Excellent interviewing, communication, and relationship-building skills. Experience working with Applicant Tracking Systems (ATS). Ability to manage multiple requisitions simultaneously while meeting hiring deadlines. Preferred Qualifications Experience recruiting for startup or high-growth technology companies. Familiarity with cloud platforms (AWS, Azure, or Google Cloud). Experience hiring professionals with expertise in Python, SQL, TensorFlow, PyTorch, Spark, Kubernetes, or similar technologies. Experience recruiting for remote and distributed engineering teams. The expected salary range for this position is $140,000 – $160,000 USD per year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: We're piloting a new type of voice work as part of a voice data collection program: live, roleplayed customer service calls. You play the business, such as a clinic receptionist or a facility assistant. A member of our training team calls you as the customer. Calls are unscripted on the caller's side, so you'll need to stay in character, follow a defined policy, and handle whatever comes up naturally. After each call, you score the caller's performance on a short online form. You're assessed on three things: how well you perform, how well you capture the audio, and how accurately you evaluate the call. What You’ll Own: Prepare in advance from two receiver scripts that you'll receive at least 24 hours before your session. Learn the beats well enough to deliver them as natural speech, not read them aloud. Take two role played calls of about 5–7 minutes each in a single 20-minute session. One is a routine call. The other is more demanding and requires you to hold a firm position politely while the caller pushes back. Deliver clear, expressive, and consistent vocal performances across calls, following the project guidelines for tone, pacing, and scenarios. State fixed facts exactly as written, including prices, hours, policies, and timeframes. Capture caller-supplied details such as names, phone numbers, dosages, and amounts precisely, and read them back accurately. Verify a caller's identity and authority before acting on an account and disclose only what the script permits. Hold a policy without inventing authority, workarounds, or exceptions, and without sounding cold or robotic. Record both sides of each call as a single mixed audio track using the provided capture equipment. Name the files exactly as specified and upload them to the designated folder shared in your briefing packet. Within 24 hours of your session, upload both recordings and complete a structured evaluation of each call: scores on a 1–5 scale, error tags with severity ratings, and specific, timestamped notes that someone who didn't hear the call could act on. You’ll Thrive in This Role If You Have Native-level English, including comfort reading numbers, names, and medication dosages back accurately. Professional experience in voice acting, acting, concierge-style or customer-facing voice work, or similar. Call-quality monitoring, QA analyst, or trainer/CSM experience in a BPO environment. Reception or front-desk experience in a clinic, property management, or storage/utilities setting. Theatre, improv, or voice-over experience. A quiet, uninterrupted space at home for the session, with no background noise or shared room. Availability for a 20-minute live session, plus preparation time before and evaluation time after. Strong attention to detail and the ability to follow precise technical and file-delivery instructions. The expected hourly salary range for this position is $25-$30p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of 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 Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. 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. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. 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. You’ll Thrive in This Role If You Have: A Bachelor’s degree or higher in a humanities specialization is required. Advanced degrees are strongly preferred (Master’s or PhD) Professional or Expert level proficiency (C1/C2) in English and Vietnamese The expected hourly salary range for this position is $15 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of 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 Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. 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. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. 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. You’ll Thrive in This Role If You Have: A Bachelor’s degree or higher in a humanities specialization is required. Advanced degrees are strongly preferred (Master’s or PhD) Professional or Expert level proficiency (C1/C2) in English and Turkish The expected hourly salary range for this position is $15-$20 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of 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 Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. 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. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. 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. You’ll Thrive in This Role If You Have: A Bachelor’s degree or higher in a humanities specialization is required. Advanced degrees are strongly preferred (Master’s or PhD) Professional or Expert level proficiency (C1/C2) in English and Russian The expected hourly salary range for this position is $15 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. About this Role: This role is a 3-month, W2, full-time contract (40 hours per week.) Scope of the Role: We are looking for detail-oriented Voice Specialists to evaluate the performance of AI models through real-time, voice-based conversations. In this role, you will interact with two different AI models using the same assigned scenario, carefully compare their responses, and provide structured evaluations based on defined quality criteria. The goal of the evaluation is to create a fair, consistent comparison between models and identify which model delivers the stronger overall conversational experience. What You’ll Own: Review an assigned scenario and roleplay a natural conversation with two different AI models . Conduct comparable conversations with Model A and Model B, keeping the scenario, conversational approach, and overall interaction as consistent as possible. Maintain approximately the same number of conversational turns with each model to support a fair comparison. Record your voice during each interaction and pay close attention to the quality and behavior of the model's responses. Evaluate each model across five defined evaluation dimensions . Identify and categorize relevant error clusters that may occur in the audio or conversation. Compare the performance of Model A and Model B based on the evaluation criteria and your observations. Select the model that demonstrated the stronger overall performance according to the defined evaluation framework. Write a detailed rationale explaining your final preference, referencing specific examples and observations from both conversations. Apply evaluation guidelines consistently across different scenarios and model interactions You’ll Thrive in This Role If You Have Bachelors Degree Strong attention to detail and ability to notice subtle differences in conversational quality. Excellent listening and comprehension skills. Strong written communication skills, particularly the ability to explain observations clearly and objectively. Ability to consistently follow detailed evaluation guidelines. Comfort speaking naturally and role-playing different conversational scenarios. Ability to compare two interactions fairly without allowing personal preferences to influence the evaluation. Strong critical-thinking and analytical skills. Reliability and consistency when completing structured evaluation tasks. Familiarity with AI assistants, voice-based AI, or conversational systems The expected hourly salary range for this position is $20-$25 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Hybrid - Washington D.C Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. About the Role: We build an advanced data platform that helps demanding enterprise and public-sector customers turn large, complex datasets into high-quality, structured data for AI. We're a small, senior team that values ownership, correctness, and strong engineering. We're hiring a full-stack engineer to help build and operate the product end to end, and to work closely with the customers who use it. You'll spend most of your time building, with regular chances to join customer conversations, help deploy the platform in their environments, and see how it's used in the field. It's a great fit for an engineer who enjoys working with customers and wants to grow into a more customer-facing role. What You'll Do: Build features end to end across our Rust backend and TypeScript/React frontend, from the data model and APIs to a polished, interactive web application. Design data models, APIs, and ingestion pipelines that are fast and reliable at scale. Help deploy, configure, and support the platform in cloud and customer environments, including AWS GovCloud, on-premises Kubernetes, and air-gapped or classified networks. Build data connectors and integrations that bring customer data into the platform and fit it to their workflows. Join customer conversations, demos, and occasional on-site visits to understand how the product is used, and turn that feedback into product improvements. Contribute to architecture, code quality, performance, and security as the product grows. What We're Looking For: 5+ years building and shipping production software. Active Top Secret clearance with SCI eligibility (U.S. citizenship required). Strong backend skills in a modern systems or application language (we work primarily in Rust; we're happy to help a strong engineer ramp up). Solid SQL and PostgreSQL fundamentals — indexing, transactions, and query performance. Proficiency with TypeScript and React and an eye for clean, responsive UIs. Experience deploying and running services with Docker and Kubernetes. Strong communication skills — able to explain technical work clearly to customers and non-technical stakeholders. Ability to travel to customer sites. Nice to Have: Customer-facing, consulting, or solutions-engineering experience. Experience deploying software in classified, air-gapped, or other regulated environments. AWS/EKS (including GovCloud), infrastructure-as-code, and STIG or RMF experience. Experience with data-intensive applications, data pipelines, or Python for data work. Familiarity with AI training data, annotation, or data-labeling workflows. Core Technologies: Rust · TypeScript / React · PostgreSQL · Python · cloud object storage · Docker · Kubernetes · AWS (including GovCloud). (Additional components shared during the interview process.) Eligibility: U.S. citizenship and an active Top Secret clearance with SCI eligibility are required. This role is open to candidates anywhere in the U.S. (remote) or hybrid in the Washington, DC area, with occasional travel to customer sites. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Hybrid - Washington D.C Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. About the Role: We build an advanced data platform that helps demanding enterprise and public-sector customers turn large, complex datasets into high-quality, structured data for AI. We're a small, senior team that values ownership, correctness, and strong engineering. We're hiring a full-stack engineer to help build and operate the product end to end, and to work closely with the customers who use it. You'll spend most of your time building, with regular chances to join customer conversations, help deploy the platform in their environments, and see how it's used in the field. It's a great fit for an engineer who enjoys working with customers and wants to grow into a more customer-facing role. What You'll Do: Build features end to end across our Rust backend and TypeScript/React frontend, from the data model and APIs to a polished, interactive web application. Design data models, APIs, and ingestion pipelines that are fast and reliable at scale. Help deploy, configure, and support the platform in cloud and customer environments, including AWS GovCloud, on-premises Kubernetes, and air-gapped or classified networks. Build data connectors and integrations that bring customer data into the platform and fit it to their workflows. Join customer conversations, demos, and occasional on-site visits to understand how the product is used, and turn that feedback into product improvements. Contribute to architecture, code quality, performance, and security as the product grows. What We're Looking For: 5+ years building and shipping production software. Active Top Secret clearance with SCI eligibility (U.S. citizenship required). Strong backend skills in a modern systems or application language (we work primarily in Rust; we're happy to help a strong engineer ramp up). Solid SQL and PostgreSQL fundamentals — indexing, transactions, and query performance. Proficiency with TypeScript and React and an eye for clean, responsive UIs. Experience deploying and running services with Docker and Kubernetes. Strong communication skills — able to explain technical work clearly to customers and non-technical stakeholders. Ability to travel to customer sites. Nice to Have: Customer-facing, consulting, or solutions-engineering experience. Experience deploying software in classified, air-gapped, or other regulated environments. AWS/EKS (including GovCloud), infrastructure-as-code, and STIG or RMF experience. Experience with data-intensive applications, data pipelines, or Python for data work. Familiarity with AI training data, annotation, or data-labeling workflows. Core Technologies: Rust · TypeScript / React · PostgreSQL · Python · cloud object storage · Docker · Kubernetes · AWS (including GovCloud). (Additional components shared during the interview process.) Eligibility: U.S. citizenship and an active Top Secret clearance with SCI eligibility are required. This role is open to candidates anywhere in the U.S. (remote) or hybrid in the Washington, DC area, with occasional travel to customer sites. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: The race to production AI is creating unprecedented demand for trusted partners who can help enterprises deploy, scale, and operationalize AI solutions. Innodata is investing heavily in expanding our strategic relationship with one of the world’s most influential technology companies, and we are seeking a Director / Client Partner to lead that growth. This is a senior, high-impact account leadership position focused on expanding and deepening Innodata’s relationship across the client’s AI, Cloud, Data, Product, Engineering, and Operations organizations. You will own the account strategy, executive relationships, revenue growth, and commercial success of one of Innodata’s most important strategic accounts. Success in this role requires a unique combination of executive presence, consultative selling, account expansion expertise, and a deep understanding of AI, data engineering, cloud, and digital transformation services. You will partner closely with client stakeholders as well as Innodata’s solution architects, delivery leaders, and AI experts to identify opportunities, shape solutions, and drive meaningful business outcomes. Your goal will be to expand Innodata’s Enterprise AI footprint across the account, transform existing relationships into larger strategic engagements, and identify new production AI workloads that create long-term value for both organizations. What You’ll Own: Own the overall Enterprise AI account strategy, growth plan, pipeline, bookings, forecasting, and revenue performance. Develop and execute account expansion strategies that uncover new opportunities across the client’s AI, Cloud, Data, Product, Engineering, Operations, and Trust & Safety organizations. Build and maintain executive-level relationships with key stakeholders across the client’s ecosystem. Identify, qualify, and close complex professional services and AI transformation engagements valued at $2M+ while contributing to long-term account growth. Drive expansion into new production AI initiatives, including Generative AI, Agentic AI, AI evaluation, data engineering, analytics, and managed AI services. Collaborate with solution architects, data scientists, engineering leaders, and delivery teams to design workshops, pilots, proofs of concept, and production-scale programs. Position and cross-sell Innodata’s capabilities across AI-powered data engineering, GenAI application development, GenBI, RAG and context engineering, AI evaluation, HITL workflows, and managed production support. Lead commercial negotiations, proposal development, opportunity qualification, and contract discussions with senior stakeholders. Build a multi-threaded account presence across technical, business, procurement, and executive teams to accelerate growth and strengthen strategic partnerships. Serve as the voice of the customer internally, helping shape Innodata’s Enterprise AI offerings and long-term go-to-market strategy. You’ll Thrive in This Role If You Have: 10+ years of experience in enterprise technology, AI, data engineering, cloud, analytics, software engineering services, or digital transformation sales and account management. 5+ years of experience managing and growing large strategic technology accounts, ideally within major technology companies or hyperscaler environments. A demonstrated track record of growing accounts to $5M–$10M+ in annual revenue and closing complex services engagements exceeding $2M. Experience selling AI, data engineering, analytics, cloud, digital transformation, or managed services solutions. Strong executive presence and the ability to engage senior leaders across business and technical organizations. Expertise in account planning, opportunity qualification, forecasting, and complex commercial negotiations. The ability to navigate multiple stakeholder groups including Engineering, Product, AI/ML, Data, Operations, Procurement, and executive leadership. A Bachelor’s degree is required; an MBA or Master’s degree is preferred. The expected salary range for this position is $180,000 – $220,000 USD per year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - Canada Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Innodata is building a team of Prompt Engineers to leverage large language models (LLMs) to automate and optimize data annotation and human evaluation workflows. In this role, you will design and implement effective prompt strategies that improve accuracy, localization, and cultural alignment in data labeling and translation processes. Working closely with Product, Data Science, Operations, and client stakeholders, you will translate business requirements into scalable AI-driven solutions. You will identify automation opportunities, develop prompt-based workflows, and continuously measure and refine performance to ensure high standards of quality and reliability. This position offers the opportunity to directly impact efficiency, scalability and innovation for a leading global technology partner. What You’ll Own: Collaborate with data scientists, linguists, and localization experts to ensure accuracy and cultural relevance. Prototype and validate AI models to demonstrate initial feasibility, potential impact, and overall effectiveness. Design, develop, and implement prompts for data labeling and localization processes within software applications. Understand the current components of the software stack, use cases and problems and iterate on solutions leveraging a solid knowledge of data structures, data formats, and data modeling. Conduct user testing and feedback analysis to optimize prompt design for data accuracy and linguistic consistency. Analyze model performance using key performance indicators (KPIs) and metrics, ensuring that AI models meet customer acceptance criteria and deliver high-quality outputs. Communicate technical findings and solution strategies to both technical and non-technical stakeholders, including presenting model performance and actionable insights in a clear, accessible manner. Collaborate on data pipelines and workflows that integrate LLMs into automated systems, enhancing both the efficiency and effectiveness of data annotation tasks. Create guidelines and training materials for prompt usage in data labeling and localization projects. Stay informed on data labeling and localization industry trends and tools to enhance prompt engineering techniques. You’ll Thrive in This Role If You Have: 2 years of prompt engineering / LLM fine-tuning, or related AI/ML roles. Familiarity with tools/platforms for annotation and human-in-the-loop workflows (e.g., Labelbox). Experience designing and automating data annotation workflows. Knowledge of data annotation and the challenges of scaling human-in-the-loop workflows. Familiarity with cloud platforms, containerization, and model deployment. Deep understanding of LLMs (e.g. transformer-based architectures). Demonstrated experience programmatically using LLMs to automate data labeling, classification, localization and annotation tasks. Strong expertise in Python for NLU, for data processing & transformation, and for statistical analysis. Familiarity with JSON, Javascript or XML. Experience with popular frameworks and libraries, including TensorFlow, PyTorch, Jupyter, and other relevant AI/ML tools. Familiarity with APIs and platforms for working with LLMs (e.g., OpenAI, Hugging Face, etc.). Knowledge of localization best practices and cultural nuances for different languages and regions. Strong understanding of LLM evaluation metrics and the ability to assess model reliability, bias, and generalizability. Experience working with data pipelines, automation tools, and integrating models into production systems to ensure scalable, reliable solutions. The expected salary range for this position is $80,000 – $90,000 CAD per year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Video is where multimodal models are weakest and hardest to grade. Temporal reasoning, long-form understanding, grounding events in time, and holding audio, video, and text together do not fall out of image benchmarks — and the evaluations for them are still immature. Closing that gap is gated as much by how we design data and evaluation as by architecture. Innodata builds that data and those evaluations for the customers and frontier labs advancing video and multimodal models, and we are hiring a Research Scientist to own the science behind it. You will partner directly with the customers and frontier labs building video understanding, video-language, and video-generation models, as interested in the data behind them as in the models themselves. Video spans two model families judged in completely different ways: models that understand video — answering questions, localizing events, grounding language in time — where the question is whether the answer is correct; and models that generate it, where fidelity, temporal coherence, and physical plausibility matter and no automatic metric is settled. You own the evaluation science for both, and knowing when model-based scoring can stand in for a human versus when it can't. Your conclusions shape what our partners measure and collect next. What You’ll Own: You will define how Innodata designs, structures, and evaluates video data for video and multimodal models, and you will validate those choices experimentally. Concretely, you will: Translate the requirements of video and multimodal models — video understanding, temporal and event localization, action recognition, long-form video, video-language models, video generation, cross-modal reasoning, and multimodal retrieval and grounding — into concrete data specifications: modalities, annotation schemas, sampling, and evaluation criteria. Build evaluation methodology for video understanding — temporal grounding accuracy, long-context and long-horizon reasoning, and dynamic multi-turn, cross-modal, and retrieval-and-grounding evaluation — clear about when model-based scoring is trustworthy and when a human is needed. Build evaluation methodology for video generation — fidelity, temporal coherence, and physical plausibility, including generative video used as a world model — the regime where automatic metrics are weakest and human judgment matters most. Decide how existing and incoming video should be structured, enriched, and sampled to extract the most model value from it, including from messy, domain-specific footage. Run experiments that prove data decisions matter: fine-tune and evaluate models on Innodata data, with ablations tying specific data choices to measurable improvement. Design adversarial and stumping evaluations that surface where video and multimodal systems fail, and turn those failures into better data. Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. Work with annotation teams, subject-matter experts, and the synthetic-data pipeline to turn specifications into operational collection and labeling plans. You’ll Thrive in This Role If You Have: Roughly 5+ years of hands-on industry experience in video understanding or multimodal ML. We weight practical experience over formal credentials; a PhD with a compelling, current research agenda can offset the lower end. A Bachelor's degree in computer science, electrical engineering, or a related technical or quantitative field is required; an advanced degree (MS or PhD) in a relevant field is preferred. Trained and evaluated video or multimodal models yourself, with strong PyTorch fundamentals. Fluency in the formats and tooling video work runs on: ffmpeg and decord pipelines, temporal and COCO-style annotation, WebDataset, Parquet and Arrow, and HuggingFace datasets. Experience fine-tuning large video or vision-language models with the modern toolchain (HuggingFace transformers, PEFT, efficient inference), and with long-form video, streaming, temporal segmentation, or synthetic video generation. A way of thinking in datasets and benchmarks: you have built evaluation sets, calibrated difficulty, and argued about what makes video data good for a given objective. A track record the field recognizes: first-author publications or strong open-source contributions at venues such as CVPR, ICCV, ECCV, NeurIPS, or ICLR. The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming — safety and robustness testing for video and multimodal systems. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Where models actually differ now is robustness across accents, noise, and code-switching; speaker diarization; the naturalness of generated speech; and latency under streaming. Measuring those honestly, and building the data that trains for them, is gated as much by data and evaluation design as by architecture. Innodata builds that data and those evaluations for the customers and frontier labs advancing speech and audio models, and we are hiring a Research Scientist to own the science behind it. You will partner directly with the customers and frontier labs building ASR, text-to-speech, speech-to-speech and conversational voice, diarization, and audio-language models, as interested in the data behind them as in the models themselves. Your work is judgment: which conditions and languages a benchmark must cover to be honest, what a transcription convention should be for a given objective, and when an automated metric can be trusted versus when a human ear is required. You will also partner closely with our transcription and linguistics lead, whose standards directly shape what the models learn. What You’ll Own: You will define how Innodata designs, structures, and evaluates audio data for speech and audio models, and you will validate those choices experimentally. Concretely, you will: Translate the requirements of speech and audio models — ASR, text-to-speech and speech generation, speech-to-speech and conversational voice, speaker diarization and verification, audio-language models, and streaming systems — into concrete data specifications: modalities, transcription and annotation schemas, sampling, and evaluation criteria. Build evaluation methodology that goes past word error rate — semantic accuracy, robustness to noise and accent, code-switching, diarization error rate (DER), naturalness and intelligibility of generated speech, and streaming latency — and know when automated metrics hold and when they don't. Decide how existing and incoming audio should be structured, enriched, and sampled for coverage that fits the model objective — across languages, accents, and acoustic conditions (studio, real-world, telephonic), speaker demographics, emotional and paralinguistic range, scripted versus spontaneous speech, and single- versus multi-speaker settings, including low-resource and code-switched speech. Partner with the transcription and linguistics lead to turn model objectives into transcription specifications, and to quantify how transcription conventions and quality move ASR and speech-model results. Partner with the audio solutions and engineering team so the audio we collect is built for the model objective: you specify what good data and evaluation require, and they scope programs with customers and capture audio to spec. Run experiments that prove data decisions matter: fine-tune and evaluate models on Innodata data, with ablations tying specific data choices to measurable improvement. Design adversarial and stumping evaluations — noisy, accented, and adversarial audio — that surface where speech systems fail, and turn those failures into better data. Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. Work with annotation teams, subject-matter experts, and the synthetic- and augmented-audio pipeline to turn specifications into operational plans. You’ll Thrive in This Role If You Have: Roughly 5+ years of hands-on industry experience in speech or audio ML. We weight practical experience over formal credentials; a PhD with a compelling, current research agenda can offset the lower end. A Bachelor's degree in computer science, electrical engineering, or a related technical or quantitative field is required; an advanced degree (MS or PhD) in a relevant field is preferred. Trained and evaluated speech or audio models yourself — ASR, TTS, speech-to-speech, speaker, or audio-language models — with strong PyTorch fundamentals. Fluency in the toolchains and metrics speech work runs on: ESPnet, NeMo, SpeechBrain, or Kaldi, HuggingFace, forced alignment, and WER/CER and the metrics that go beyond them. Hands-on experience with multilingual, accented, dialectal, low-resource, or code-switched speech, and with synthetic or augmented audio (TTS pipelines, noise and room-response simulation). A way of thinking in datasets: you have built evaluation sets, reasoned about coverage across conditions, and argued about what makes speech data good for a given objective. A track record the field recognizes: first-author publications or strong open-source contributions at venues such as Interspeech, ICASSP, ASRU, SLT, or NeurIPS. The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming — for example spoofing and voice-cloning robustness, or bias across accents and languages. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Progress in physical AI is gated as much by data design as by model architecture. The best manipulation and locomotion policies, vision-language-action models, and world models are only as good as the trajectories, demonstrations, and environments they learn from — so the highest-leverage decisions are made long before a model trains: what to capture, what to synthesize, how to represent an action, and how to know whether any of it will transfer to a real robot. Innodata is building the data and evaluation practice behind the next generation of robotics foundation models, in direct partnership with the customers and frontier labs defining that frontier. We are hiring a Research Scientist to own the science of that data. You will partner directly with the customers and frontier labs building robot foundation models, as interested in the data those models learn from as in the models themselves. Your leverage is scientific judgment about data: deciding what is worth capturing in the real world versus generating in simulation, how to weight a training mix across embodiments and sensors, and how to measure whether a policy will hold up outside the lab. Your conclusions will shape what our partners collect next. What You’ll Own: You will define how Innodata designs, structures, and evaluates data for robot foundation models, and you will validate those choices experimentally. Concretely, you will: Translate the requirements of robotics foundation models — vision-language-action models, world models, and manipulation and locomotion policies — into concrete data specifications: modalities, action representations (tokenization, chunking, diffusion and flow-matching action experts), sampling, annotation schemas, and evaluation criteria. Decide what is worth capturing in the real world versus generating in simulation, and curate and weight training mixes across heterogeneous robot datasets that span different embodiments, action spaces, and sensor setups. Guide collection across capture modalities — motion capture, egocentric, exocentric, teleoperation, and multi-sensor — and across synthetic pipelines, so that what we produce maps cleanly to model needs rather than accumulating for its own sake. Build evaluation and benchmarking methodology that predicts real-world transfer — coverage, discriminative power, reliability, and sim-to-real fidelity — including world-model evaluation (rollout quality and action-conditioned prediction) and the domain-randomization and system-identification choices that close the sim-to-real gap. Run the experiments that prove it: fine-tune and evaluate foundation models on Innodata data, with data-quality ablations and scaling studies that demonstrate specific data decisions produce measurable model improvement. Design adversarial and long-horizon evaluations that surface where policies and world models break, and turn those failure modes into better data. Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. Collaborate with the capture lab, annotation teams, and the synthetic-data pipeline to turn specifications into operational collection and labeling plans. You’ll Thrive in This Role If You Have: Roughly 4+ years of hands-on industry experience in robot learning or robotics ML. We weight practical experience over formal credentials; because the field is young, a PhD with a compelling, current research agenda can offset the lower end, and we will also consider very senior candidates with the right background. A Bachelor's degree in computer science, electrical engineering, robotics, or a related technical field is required; an advanced degree (MS or PhD) in a relevant field is preferred. Trained and evaluated robot policies yourself — manipulation or locomotion via imitation learning or reinforcement learning — with strong PyTorch fundamentals. You build and measure models, you don't just call them. A way of thinking in datasets: you have curated, filtered, and weighted robot data across embodiments and sensors, and you have real opinions about what makes it good for a given objective. Fluency in robotics data formats and standards — the LeRobot dataset format, RLDS, and Open X-Embodiment — along with common motion and sensor formats. Hands-on experience with simulation and synthetic data — NVIDIA Isaac Sim, Isaac Lab, and Omniverse, or MuJoCo and comparable engines — including domain randomization, system identification, and sim-to-real transfer. Experience with teleoperation or egocentric data collection, and comfort adapting VLM backbones for control and fine-tuning large VLA models with the modern toolchain (HuggingFace transformers, PEFT). A track record the field recognizes: first-author publications or strong open-source contributions at venues such as CoRL, ICRA, IROS, RSS, or NeurIPS. The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming for embodied systems, for example safety and robustness testing. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: As speech and audio models get better, the human role gets harder, not easier — it moves from producing transcripts to defining what a correct one is, adjudicating the cases models still get wrong, and designing the human-in-the-loop workflows that keep improving them. Innodata runs high-volume segmentation and transcription workflows for the customers and frontier labs building these models, and we are hiring a principal-level linguist to own the linguistic standards and quality behind that work — today, and as the workflows evolve alongside the models over the next two years. This is the applied-expert counterpart to our Speech & Audio Research Scientist. You set the standards the models are trained and measured against, and you understand the big picture: how different transcription and segmentation methods change what a model learns, and how that ripples into the speech and content-understanding systems our partners are building. You know the research and you know the tools — from IPA and acoustic analysis to forced alignment and the ASR engines our partners benchmark against — but your leverage is linguistic judgment and standard-setting at scale, not building models yourself. What You’ll Own: You will own the linguistic foundation of Innodata's segmentation and transcription work across languages, domains, and use cases. Concretely, you will: Define transcription and segmentation standards, style guides, and annotation conventions — verbatim and clean/intelligent verbatim, IPA and phonetic transcription, timestamping and boundary segmentation, speaker labeling and diarization labels, disfluencies and non-speech events, code-switching, and orthographic conventions. Establish and run the quality frameworks behind that work: rubrics, error taxonomies, adjudication processes, inter-annotator agreement, and human QA at scale. Own the quality lifecycle for transcription and segmentation deliverables end to end — pre-processing and normalization of incoming data, quality checks at the point of acceptance, post-processing and pre-delivery validation against spec, and report creation and packaging for delivery — partnering with delivery operations on execution at scale. Design the human-in-the-loop workflows themselves — deciding where human review, correction, and adjudication add the most value as ASR quality rises, so our experts spend their time on what the models still can't do rather than on what they already can. Handle the linguistically hard cases models fail on — accented and dialectal speech, low-resource and multilingual audio, overlapping speech, domain jargon (medical, legal, technical), and noisy acoustic conditions. Partner with the Speech & Audio Research Scientist to turn model objectives into transcription and segmentation specifications, and to work out how different transcription methods — verbatim versus clean, phonetic versus orthographic, and how audio is segmented and labeled — affect the training and evaluation of ASR, TTS, and speech and content-understanding models. Train, calibrate, and mentor expert transcribers and reviewers, and build the onboarding and calibration that keep quality consistent as the work scales. Represent Innodata's transcription and segmentation approach to the customers and frontier labs we partner with, and contribute to the methodology and best-practice documentation that make our work legible to their teams. You’ll Thrive in This Role If You Have: Substantial industry experience (typically 8+ years) in transcription, segmentation, and speech-data quality — enough that you have authored standards, not only followed them. This is a principal-level role, and we weight practical depth heavily. A Bachelor's degree in linguistics, phonetics, or computational linguistics, or a closely related field, is required — with a strong foundation in phonetics, phonology, and sociolinguistics so that IPA, prosody, disfluency, dialect, and register are native concepts. An advanced degree is preferred. A big-picture grasp of how transcription and segmentation choices flow downstream into modeling — how different methods change what speech and content-understanding models learn, and therefore which method fits which modeling objective. You can explain to a model builder why a transcription decision matters. Fluency in phonetic transcription and IPA, plus hands-on experience with acoustic and phonetic analysis of speech — spectrograms, formants, pitch and prosody, and segment boundaries — applied to real, messy speech data at scale (for example in Praat). Deep experience with audio segmentation and its conventions — utterance and turn boundaries, timestamping, and speaker and diarization labeling — across real-world audio. Hands-on fluency with the modern speech stack: Whisper and the commercial ASR engines your partners benchmark against (such as AssemblyAI, Deepgram, Rev, and Speechmatics), forced alignment (for example the Montreal Forced Aligner), and annotation tools such as ELAN. Comfort scripting for speech-data work — Python for batch processing, QA, and metrics such as inter-annotator agreement and WER, plus regular expressions and Praat scripting — enough to work fluently with data and pipelines without needing an engineer for every task. Practical data-management skills across the delivery lifecycle — pre-processing, acceptance-stage quality checks, post-processing, pre-delivery validation, and report creation and packaging — so deliverables leave the door correct, consistent, and well documented. Multilingual capability and hands-on experience with accented, dialectal, and code-switched speech; low-resource languages a strong plus. A point of view on how human-in-the-loop workflows should evolve as models improve — where humans stay in the loop, where they move up to adjudication and standard-setting, and how to measure the difference. Strong written and verbal communication, comfortable working directly with research scientists and interfacing with the customers and frontier labs we partner with. Bonus: responsible-AI considerations for speech, such as bias across accents and dialects and privacy and consent in voice data. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - Alabama ; Remote - Alaska ; Remote - Arkansas ; Remote - Delaware ; Remote - Florida ; Remote - Georgia ; Remote - Indiana ; Remote - Maryland ; Remote - Massachusetts ; Remote - New Hampshire ; Remote - New Jersey ; Remote - New York ; Remote - South Carolina ; Remote - Texas Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: We are looking for detail-oriented Voice Specialists to evaluate the performance of AI models through real-time, voice-based conversations. In this role, you will interact with two different AI models using the same assigned scenario, carefully compare their responses, and provide structured evaluations based on defined quality criteria. The goal of the evaluation is to create a fair, consistent comparison between models and identify which model delivers the stronger overall conversational experience. What You’ll Own: Review an assigned scenario and roleplay a natural conversation with two different AI models . Conduct comparable conversations with Model A and Model B, keeping the scenario, conversational approach, and overall interaction as consistent as possible. Maintain approximately the same number of conversational turns with each model to support a fair comparison. Record your voice during each interaction and pay close attention to the quality and behavior of the model's responses. Evaluate each model across five defined evaluation dimensions . Identify and categorize relevant error clusters that may occur in the audio or conversation. Compare the performance of Model A and Model B based on the evaluation criteria and your observations. Select the model that demonstrated the stronger overall performance according to the defined evaluation framework. Write a detailed rationale explaining your final preference, referencing specific examples and observations from both conversations. Apply evaluation guidelines consistently across different scenarios and model interactions You’ll Thrive in This Role If You Have Bachelors Degree Strong attention to detail and ability to notice subtle differences in conversational quality. Excellent listening and comprehension skills. Strong written communication skills, particularly the ability to explain observations clearly and objectively. Ability to follow detailed evaluation guidelines consistently. Comfort speaking naturally and roleplaying different conversational scenarios. Ability to compare two interactions fairly without allowing personal preferences to influence the evaluation. Strong critical-thinking and analytical skills. Reliability and consistency when completing structured evaluation tasks. Familiarity with AI assistants, voice-based AI, or conversational systems The expected hourly salary range for this position is $20-$25 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: In Office - San Jose, California Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: We're building a dedicated reviewer team to evaluate complex, real-world agentic AI workflows, assessing whether AI agents complete tasks safely, respect user intent and consent, and hold up under close scrutiny. This isn't routine content moderation: reviewers work through ambiguous, multi-step scenarios where judgment calls matter as much as checklists. You'll operate inside isolated test environments, apply structured rubrics, and help calibrate what "safe and policy adherent agent behavior" actually looks like. What You’ll Own: Review multi-step agent task trajectories against rubric-based criteria covering task success, safety, and policy adherence Evaluate whether an agent respected user agency and informed consent — not just whether it followed literal instructions Apply privacy guardrails when reviewing tasks involving sensitive or personal data Work inside isolated test environments, including verifying environment resets between test runs to prevent cross-contamination Flag and escalate safety-relevant or ambiguous findings through defined escalation paths You’ll Thrive in This Role If You Have: Bachelor's degree or equivalent practical experience 2–5+ years in quality review, QA/QC, trust & safety, content moderation, data annotation, or a similarly judgment-intensive review role Strong written communication – you can justify a scoring decision clearly enough for someone else to audit it Comfort with ambiguity and structured decision-making under a rubric, rather than needing a fixed rulebook for every case Baseline understanding of AI/ML concepts and how AI agents complete tasks (can be learned through onboarding, but some familiarity helps) The expected hourly salary range for this position is $50-$55p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .