InnoData

Artificial Intelligence

• 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.

Generative AI Annotator

Data Engineer

• Design and implement data-driven solutions on GCP including BigQuery, Cloud Storage, Dataflow, Pub/Sub, and Looker/BI.
• Build ETL scripts using SQL and Python to extract, clean, and transform structured and unstructured data from ERP, procurement, logistics, and facility management systems.
• Develop and optimize data pipelines for ingestion, transformation, and loading into enterprise data lakes and warehouses.
• Build and extend end-to-end data and BI solutions, spanning extraction, storage, transformation, and visualization layers.
• Partner with supply chain, real estate, and AI/ML teams to provide pipelines for AI solutions (e.g., RAG ingestion, Copilot integration, multi-agent workflows).
• Ensure data governance, lineage, and compliance across supply chain datasets.
• Continuously optimize query performance, ETL processes, and pipeline reliability.

• Receive, validate, ingest, and ontology-map the ODIN mission-aligned corpus from AFS delivery
• Produce the ODIN load report: corpus description, ontology mapping, readiness state
• Configure CVAT annotation pipeline against the Phase 1 starter kit rule pack
• Operate both self-service and lightweight white-glove annotation paths during Phase D corpus production
• Produce 50–100 label demonstration corpus across synthetic and mission-aligned content
• Support QA/Evaluation Lead on QC execution and corpus annotation dry-runs
• Associate DataCard provenance records with annotated and synthetic outputs in coordination with the Solution Architect.

Data Annotation Engineer

Data Entry

• 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.
• 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.
• 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.
• Generating responses to prompts or questions using a language model or other AI system.
• Rewriting existing text while preserving the original meaning.
• 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.

Data AI Annotator

Solutions Engineer

• Implement Databricks REST API write-back for labeled outputs, DataCard provenance records, and synthetic content outputs
• Build and validate the Duality back-end integration: catalog intake form configuration, parameter translation to Duality generation API, async job lifecycle management (submission, tracking, retry, completion), output handling
• Implement ServiceNow REST integration for white-glove routing intake path
• Build and maintain storefront backend services (API layer between frontend UI and platform components)
• Implement DataCard write-back path in coordination with Solution Architect
• Design and maintain async job queue management and retry logic across all integration surfaces
• Structure output metadata for DataCard provenance records per DoD distribution statement requirements

Backend, Integration Engineer

• Partner with customers in presales to understand their model objectives, current data gaps, and technical constraints.
• Shape requirements: define acoustic specs, language/accent coverage, speaker demographics, emotional/paralinguistic range, transcript and metadata schema, and QA targets (WER/DER, LUFS, etc.).
• Translate requirements into scoped execution plans — volumes, timelines, methodology, pricing inputs — in partnership with delivery.
• Serve as the credible technical voice in the room: explain tradeoffs (studio vs. real-world vs. telephonic, scripted vs. spontaneous, single vs. multi-speaker) and defend methodology choices.
• Build reusable solutioning assets: scoping frameworks, spec templates, reference architectures for common audio data use cases.
• Stay at the tip of the spear on speech-AI developments (TTS, ASR, speech-to-speech) and what data the next generation of models will need.
• Produce go-to-market material: technical blog posts, white papers, benchmark reports, and reference content that demonstrates Innodata's depth.
• Represent Innodata externally: speak at and work conferences (Interspeech, ICASSP, industry events), engage the speech-AI community, and build our public technical profile.
• Feed market intelligence back into strategy — advise on emerging data categories and where to invest ahead of demand.

Audio Solutions Architect

Engineer

• Own the end-to-end audio signal chain and post-processing pipeline for all collection programs.
• Define and document technical specifications: sample rates, bit depth, formats, loudness (LUFS) targets, noise floors, channel configurations.
• Design and maintain the “Innodata sound signature” — a consistent, spec-compliant acoustic profile across studio, remote, real-world, and telephonic captures.
• Build technical QA: automated and manual checks that validate audio against spec before delivery.
• Specify and validate recording setups for vendors and remote contributors (signal-chain testing in a small in-house studio).
• Partner with the Solutions Architect to translate customer acoustic requirements into achievable technical recipes.
• Drive tooling: help select and configure recording/QA/processing tools; automate where possible.
• Troubleshoot acoustic and signal issues across diverse capture environments.

Audio Engineer

Research Scientist

• Lead research and experimentation on how evaluation design, measurement strategies, and feedback signals influence model improvement.
• Help define the next generation of evaluation-driven model improvement workflows.
• Study how different evaluation approaches (human, automated, hybrid) shape model selection and post-training outcomes.
• Design experiments that produce credible, actionable conclusions.
• Support customer engagements by bringing scientific rigor to evaluation strategy, methodology review, and technical recommendations.
• Define and execute a research agenda focused on LLM evaluation and post-training, especially evaluation-driven model improvement.
• Design rigorous experiments to study how evaluation methodologies impact fine-tuning and post-training outcomes.
• Develop and validate evaluation frameworks for LLM and multimodal systems.
• Analyze model behavior and failure patterns; generate actionable recommendations for model improvement and evaluation redesign.
• Collaborate with AI/ML Research Engineers to translate research methods into scalable evaluation and post-training pipelines.

Applied Research Scientist, LLM Evaluation – Post-Training

Data Scientist

• Translate customer goals — such as improving differential diagnosis, evaluating a clinical note summarizer, testing a RAG-based medical literature assistant, or creating preference data for patient-facing chatbots — into dataset specifications, taxonomies, rubrics, sampling plans, and acceptance criteria.
• Make multimodal health AI a core focus: design training and evaluation datasets across clinical text, medical images, waveforms, structured EHR data, claims, trial data, medical literature, patient communications, payer policies, drug information, and other clinical artifacts, as well as use cases such as clinical reasoning, medical QA, note summarization, medical coding, patient communication, utilization management, and literature synthesis.
• Design evaluations for retrieval-augmented and source-grounded health AI systems, including evidence citation, faithfulness, contraindication handling, guideline adherence, source freshness, and failure modes caused by incomplete, conflicting, or stale context.
• Define sampling strategies, label schemas, inter-annotator agreement targets, adjudication workflows, SME review patterns, and quality thresholds in partnership with Language Data Scientists, clinicians, biomedical experts, and quality teams.
• Build statistical and ML checks that make healthcare datasets trustworthy: stratified sampling across specialties and patient subgroups, bias and representation analysis, leakage detection, distribution shift checks, uncertainty estimates, reliability metrics, and subgroup performance analysis.
• Partner with Applied Research Scientists and AI/ML Research Engineers to instrument datasets into evaluation and post-training pipelines, including rubric-grounded LLM-as-judge prompts, regression suites, model comparison workflows, experiment tracking, and model-improvement feedback loops.
• Evaluate health AI behavior beyond surface accuracy: calibration, hallucination on safety-critical content, refusal appropriateness, robustness under ambiguity, equity across patient subgroups, and safe handoff in agentic or workflow-integrated systems. Reason concretely about clinical workflow fit: where outputs enter care delivery, what evidence a clinician or reviewer would need to trust them, when uncertainty must be surfaced, and how patient-facing, clinician-facing, payer, pharma, and operational use cases differ in risk.
• Own data quality from source intake through delivery, including de-identified clinical text, medical literature, synthetic cases, structured records, client policies, and knowledge bases, with attention to PHI/PII handling, provenance, audit trails, versioning, and compliance documentation.
• Stay current on the health AI landscape — regulatory developments such as FDA guidance on AI/ML-enabled medical devices and EU AI Act health provisions, benchmark releases such as MedQA, MedMCQA, and HealthBench, and emerging clinical evaluation methodology.
• Support customer discovery and proposal work by scoping dataset programs, sizing annotation and SME review effort, identifying regulatory or data-access constraints, and explaining methodology choices to client clinical and ML leadership.
• Contribute to Innodata internal IP: reusable health-domain taxonomies, evaluation rubrics, golden datasets, clinical review playbooks, dataset quality checks, and methodology templates.

Applied Data Scientist, Health AI Evaluation, Datasets

• Translate customer goals — such as improving financial reasoning, building an eval suite for earnings-call summarization, or evaluating an AML/fraud copilot — into concrete dataset specifications, taxonomies, rubrics, and acceptance criteria.
• Design training and evaluation datasets across the financial AI surface: financial QA, filings and earnings analysis, credit and underwriting, fraud/AML investigation, and compliance, among other financial workflows.
• Foreground unstructured and multimodal financial data in dataset design — PDFs, scanned statements, tables, charts, and call transcripts — used by analysts, advisors, compliance reviewers, and operations teams.
• Design datasets and evaluations for retrieval-augmented and source-grounded systems: evidence citation and faithfulness to source documents, data freshness, conflict resolution across sources, and failure modes caused by incomplete or incorrectly parsed context.
• Evaluate agentic and workflow-integrated financial AI systems: tool use, retrieval, transaction boundaries, escalation behavior, and controls that prevent unsafe or unauthorized actions.
• Develop evaluation methodology that goes beyond surface accuracy — numerical consistency, hallucination rates on high-risk claims, refusal and escalation appropriateness, robustness under ambiguity, and fairness across protected or sensitive customer segments.
• Define sampling strategies, label schemas, and adjudication workflows with Language Data Scientists and finance SMEs; write annotation guidelines that make subjective finance-domain judgments explicit, calibratable, and auditable.
• Build the statistical and ML tooling that makes large financial datasets trustworthy: stratified sampling across products, markets, and modalities; bias analysis; leakage detection; and distribution shift checks, among other reliability checks.
• Build evaluation and dataset-quality evidence to support financial-services model risk management: assumptions, limitations, validation results, and residual risks, packaged as reproducible evidence.
• Partner with the AI/ML Research Engineer to instrument datasets into training, evaluation, and monitoring pipelines — rubric-grounded LLM-as-judge prompts, regression suites, and continuous monitoring.
• Own data quality end-to-end, from intake through delivery: PII handling, provenance tracking, versioning, and modality-specific QA checks.
• Reason about financial workflow context: where AI outputs enter analyst, advisor, compliance, risk, or customer-facing workflows; what evidence a reviewer needs to trust them; and when uncertainty must be surfaced.
• Support the Technical Solutions Architect during customer discovery and proposals: scoping dataset programs, sizing annotation effort, and explaining methodology to client stakeholders.
• Stay current on the financial AI landscape: regulatory developments, benchmark releases, and emerging evaluation methodology for finance-domain models.
• Contribute to Innodata internal IP: reusable taxonomies, evaluation rubrics, golden datasets, and methodology templates.

Applied Data Scientist, Finance AI Evaluation, Datasets

• 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.

InnoData

Generative AI Annotator

Data Engineer

Data Annotation Engineer

Data AI Annotator

Backend, Integration Engineer

Audio Solutions Architect

Audio Engineer

Applied Research Scientist, LLM Evaluation – Post-Training

Applied Data Scientist, Health AI Evaluation, Datasets

Applied Data Scientist, Finance AI Evaluation, Datasets

Applied AI Associate

Applied AI Analyst

AI Workflow Specialist

AI Workflow Associate

AI Training Specialist