Innodatainc

<div>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.</div>Scope of the Role: 
We are seeking a Data Engineer to design and build enterprise data warehouses, data lakes, and pipelines that power data-driven decision-making for data center supply chain and real estate operations. This role is responsible for creating scalable, secure, and optimized ETL infrastructure on GCP/AWS, while enabling advanced AI/ML use cases such as RAG, copilots, and agentic AI for predictive analytics and workflow automation.
What You’ll Own:
<ul>
<li>Design and implement data-driven solutions on GCP including BigQuery, Cloud Storage, Dataflow, Pub/Sub, and Looker/BI.</li>
<li>Build ETL scripts using SQL and Python to extract, clean, and transform structured and unstructured data from ERP, procurement, logistics, and facility management systems.</li>
<li>Develop and optimize data pipelines for ingestion, transformation, and loading into enterprise data lakes and warehouses.</li>
<li>Build and extend end-to-end data and BI solutions, spanning extraction, storage, transformation, and visualization layers.</li>
<li>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).</li>
<li>Ensure data governance, lineage, and compliance across supply chain datasets.</li>
<li>Continuously optimize query performance, ETL processes, and pipeline reliability.</li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>Advanced proficiency in SQL (complex queries, optimization) and Python (data engineering, scripting, APIs). </li>
<li>Experience building ETL/ELT pipelines operating on structured and unstructured data sources. </li>
<li>Knowledge of enterprise data warehouse and data lake architectures. </li>
<li>Exposure to data pipelines for AI/ML (vector DB ingestion, embeddings, RAG pipelines, copilots, agents). </li>
<li>Familiarity with supply chain or data center operations data is a strong plus. </li>
<li>Bonus: experience with ML Engineering, data visualization tools (Looker, Tableau, Power BI) and MLOps practices.  </li>
<li>Strong hands-on expertise with GCP services: BigQuery, Dataflow, Pub/Sub, Cloud Storage, Looker/BI (or similar, preferred). </li>
</ul>
 
 <div>
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 <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Data Engineer

<div>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.</div>
Scope of the Role: 
Innodata is expanding its GenAI research capability to advance state-of-the-art evaluation and post-training methods for LLM and multimodal systems. As an Applied Research Scientist, LLM Evaluation &amp; Post-Training, you will lead research and experimentation on how evaluation design, measurement strategies, and feedback signals influence model improvement.
This role is ideal for a technically rigorous researcher who is deeply fluent in modern LLM evaluation and post-training, and who can turn research insight into practical methods for customer solutions and internal platform innovation. You will work across human-in-the-loop and AI-augmented workflows, partnering with Language Data Scientists and AI/ML Research Engineers to design and validate evaluation frameworks that drive measurable model gains.
The ideal candidate combines strong experimental and statistical judgment with hands-on technical ability and can engage as a peer with research and engineering stakeholders at leading AI companies.
What You’ll Own:
As an Applied Research Scientist, LLM Evaluation &amp; Post-Training, you will help define the next generation of evaluation-driven model improvement workflows. You will study how different evaluation approaches (human, automated, hybrid) shape model selection and post-training outcomes, and you will design experiments that produce credible, actionable conclusions.
Your work may include designing benchmark datasets, developing evaluation taxonomies and protocols, defining metrics and scoring methodologies, analyzing failure modes, and testing how changes in evaluation setup affect downstream fine-tuning results. You will also support customer engagements by bringing scientific rigor to evaluation strategy, methodology review, and technical recommendations.
This is a highly collaborative role that sits at the intersection of research, engineering, and language/data operations. Additional responsibilities include (but are not limited to):
<ul>
<li>Define and execute a research agenda focused on LLM evaluation and post-training, especially evaluation-driven model improvement</li>
<li>Design rigorous experiments to study how evaluation methodologies impact fine-tuning and post-training outcomes</li>
<li>Develop and validate evaluation frameworks for LLM and multimodal systems, including:</li>
<ul>
<li>benchmark/task design</li>
<li>scoring methods</li>
<li>judge/model-assisted evaluation</li>
<li>human evaluation protocols</li>
<li>robustness/stress testing</li>
</ul>
<li>Lead research on advanced evaluation domains, including long-context, cross-modal, and dynamic multi-turn evaluations</li>
<li>Study the effectiveness and limitations of existing evaluation techniques, and propose improved methodologies with clear validity and scalability tradeoffs</li>
<li>Analyze model behavior and failure patterns; generate actionable recommendations for model improvement and evaluation redesign</li>
<li>Collaborate with AI/ML Research Engineers to translate research methods into scalable evaluation and post-training pipelines</li>
<li>Collaborate with Language Data Scientists to integrate human-in-the-loop and synthetic data/evaluation strategies into research programs</li>
<li>Engage with customer technical stakeholders to understand evaluation goals, review methodologies, and provide expert recommendations</li>
<li>Contribute to internal benchmark datasets, evaluation frameworks, and reusable research assets</li>
<li>Produce high-quality technical documentation, internal research reports, and client-facing materials explaining methods, results, assumptions, and limitations</li>
<li>Contribute to thought leadership and best practices in LLM evaluation, post-training, and GenAI quality measurement</li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>MS/PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, AI, or a related quantitative scientific field (PhD strongly preferred)</li>
<li>5+ years of relevant experience in applied research / research science in ML/AI, with substantial work in LLMs or foundation models</li>
<li>Demonstrated experience with LLM evaluation, benchmarking, alignment, post-training, or model quality research</li>
<li>Strong foundation in experimental design, statistical analysis, and scientific reasoning for ML systems</li>
<li>Strong coding skills in Python for research experimentation and analysis (e.g., data processing, evaluation pipelines, statistical analysis, visualization)</li>
<li>Experience working with modern ML tooling/frameworks (e.g., PyTorch, Hugging Face, JAX/TensorFlow as applicable) sufficient to design and execute model/evaluation experiments</li>
<li>Ability to evaluate and compare human and automated evaluation methods, including tradeoffs in cost, reliability, validity, and scalability</li>
<li>Experience designing evaluation studies and protocols that are reproducible across datasets, model versions, and evaluation runs</li>
<li>Ability to collaborate directly with technical stakeholders including research scientists, ML engineers, data scientists, and customer technical counterparts</li>
<li>Strong communication skills and ability to present nuanced technical conclusions, assumptions, and limitations clearly</li>
</ul>
The expected salary range for this position is $245,000 – $315,000 CAD per year, based on experience, skills, and qualifications.
 
<div>
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 <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Applied Research Scientist, LLM Evaluation & Post-Training

<div>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.</div> 
Scope of the Role: 
Financial services is one of the highest-stakes domains for generative AI. Numerical accuracy, regulatory compliance, model risk management, auditability, and customer harm prevention, among other concerns, are the bar for shipping anything real. Innodata partners with foundation model labs, banks, asset managers, fintechs, and other enterprise AI teams building LLMs, multimodal systems, and AI agents for financial workflows. 
As an Applied Data Scientist, Financial AI Evaluation &amp; Datasets, you own the design, measurement quality, and domain validity of the datasets used to train, fine-tune, evaluate, and monitor financial-domain LLMs, vision-language models, multimodal document models, and AI agents. You bring financial-domain fluency and data science rigor: you can read a risk policy, financial statement, or customer transcript, among other financial-services documents; turn it into a measurable dataset and evaluation specification; define what correct, grounded, compliant, and safe mean for the use case; and produce evidence that sophisticated financial-services customers, model-risk teams, and AI governance stakeholders can trust. 
This role has a special emphasis on unstructured and multimodal financial data — PDFs, scanned documents, spreadsheets, charts, call transcripts, and other mixed-document workflows where text, numbers, visuals, and metadata all matter. You will work in a pod with a Technical Solutions Architect (scopes the engagement), an Applied Research Scientist (shapes evaluation methodology), an AI/ML Research Engineer (builds training and evaluation infrastructure), and Language Data Scientists (run annotation at scale), making sure what the team produces is domain-valid, statistically defensible, compliant, auditable, and useful for evaluation and post-training. 
What You’ll Own:
<ul>
<li>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.</li>
<li>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.</li>
<li>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.</li>
<li>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.</li>
<li>Evaluate agentic and workflow-integrated financial AI systems: tool use, retrieval, transaction boundaries, escalation behavior, and controls that prevent unsafe or unauthorized actions.</li>
<li>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.</li>
<li>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.</li>
<li>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.</li>
<li>Build evaluation and dataset-quality evidence to support financial-services model risk management: assumptions, limitations, validation results, and residual risks, packaged as reproducible evidence.</li>
<li>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.</li>
<li>Own data quality end-to-end, from intake through delivery: PII handling, provenance tracking, versioning, and modality-specific QA checks.</li>
<li>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.</li>
<li>Support the Technical Solutions Architect during customer discovery and proposals: scoping dataset programs, sizing annotation effort, and explaining methodology to client stakeholders.</li>
<li>Stay current on the financial AI landscape: regulatory developments, benchmark releases, and emerging evaluation methodology for finance-domain models.</li>
<li>Contribute to Innodata internal IP: reusable taxonomies, evaluation rubrics, golden datasets, and methodology templates. </li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>5+ years of data science experience, with at least 2+ years in financial services, fintech, banking, or a comparable regulated data environment.</li>
<li>Real working knowledge of financial data and workflows: financial statements, SEC filings, transaction data, and other common financial-services document types.</li>
<li>Hands-on experience with unstructured and multimodal financial data — some combination of PDFs, scanned documents, spreadsheets, charts, or call transcripts.</li>
<li>Familiarity with financial standards or protocols such as XBRL, ISO 20022, or GAAP/IFRS reporting concepts, etc. is strongly preferred.</li>
<li>Hands-on experience designing datasets for ML — not just consuming them. You have written annotation guidelines, sized cohorts, set quality thresholds, and shipped data that downstream teams could actually train, evaluate, or monitor on.</li>
<li>Familiarity with LLM-based and multimodal financial AI workflows: prompt design, rubric-based evaluation, RAG, LLM-as-judge methods, and the limitations of automated evaluation in high-stakes contexts.</li>
<li>Strong Python and SQL; comfort with pandas, scikit-learn, or equivalent; working familiarity with Hugging Face, PyTorch, or model APIs.</li>
<li>Statistical literacy: sampling design, inter-annotator agreement metrics (e.g., Cohen's kappa), confidence intervals, and the ability to push back when a number is being over-interpreted.</li>
<li>Solid grasp of financial services privacy, compliance, and governance: PII handling, GLBA or equivalent privacy regimes, MNPI sensitivity, and documentation fit for regulated AI programs.</li>
<li>Excellent collaboration skills — upstream with a Technical Solutions Architect, sideways with research scientists and engineers, and downstream with SME annotators and quality teams.</li>
<li>A bias toward financial workflow realism. You would rather build a smaller dataset that reflects what analysts, advisors, or customers actually see than a larger one that looks impressive on paper but fails in practice.</li>
<li>Degree in a relevant field — statistics, data science, economics, finance, or a related quantitative field, or equivalent demonstrated experience. Formal finance credentials aren't required, but CFA, FRM, or MBA backgrounds, etc. are especially encouraged.</li>
<li>Experience designing evaluations for LLMs, VLMs, or multimodal models in financial reasoning, filings analysis, or fraud/AML contexts.</li>
<li>Experience with document AI, OCR/post-OCR quality, or table and chart extraction for complex financial documents.</li>
<li>Familiarity with agentic evaluation, AI observability, experiment tracking, or tools such as Weights &amp; Biases or LangFuse.</li>
<li>Familiarity with model risk management frameworks, validation documentation, fairness/bias auditing, or consumer protection analysis.</li>
<li>Experience with multilingual or cross-border financial data, or published/open-source work in financial AI or model governance. </li>
</ul>
The expected salary range for this position is $210,000 – $245,000 CAD per year, based on experience, skills, and qualifications.
 
 <div>
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 <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Applied Data Scientist, Finance AI Evaluation & Datasets 

<div>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.</div>Scope of the Role: 
Innodata is expanding its GenAI research capability to advance state-of-the-art evaluation and post-training methods for LLM and multimodal systems. As an Applied Research Scientist, LLM Evaluation &amp; Post-Training, you will lead research and experimentation on how evaluation design, measurement strategies, and feedback signals influence model improvement.
This role is ideal for a technically rigorous researcher who is deeply fluent in modern LLM evaluation and post-training, and who can turn research insight into practical methods for customer solutions and internal platform innovation. You will work across human-in-the-loop and AI-augmented workflows, partnering with Language Data Scientists and AI/ML Research Engineers to design and validate evaluation frameworks that drive measurable model gains.
The ideal candidate combines strong experimental and statistical judgment with hands-on technical ability and can engage as a peer with research and engineering stakeholders at leading AI companies.
What You’ll Own:
As an Applied Research Scientist, LLM Evaluation &amp; Post-Training, you will help define the next generation of evaluation-driven model improvement workflows. You will study how different evaluation approaches (human, automated, hybrid) shape model selection and post-training outcomes, and you will design experiments that produce credible, actionable conclusions.
Your work may include designing benchmark datasets, developing evaluation taxonomies and protocols, defining metrics and scoring methodologies, analyzing failure modes, and testing how changes in evaluation setup affect downstream fine-tuning results. You will also support customer engagements by bringing scientific rigor to evaluation strategy, methodology review, and technical recommendations.
This is a highly collaborative role that sits at the intersection of research, engineering, and language/data operations. Additional responsibilities include (but are not limited to):
<ul>
<li>Define and execute a research agenda focused on LLM evaluation and post-training, especially evaluation-driven model improvement</li>
<li>Design rigorous experiments to study how evaluation methodologies impact fine-tuning and post-training outcomes</li>
<li>Develop and validate evaluation frameworks for LLM and multimodal systems, including:</li>
<ul>
<li>benchmark/task design</li>
<li>scoring methods</li>
<li>judge/model-assisted evaluation</li>
<li>human evaluation protocols</li>
<li>robustness/stress testing</li>
</ul>
<li>Lead research on advanced evaluation domains, including long-context, cross-modal, and dynamic multi-turn evaluations</li>
<li>Study the effectiveness and limitations of existing evaluation techniques, and propose improved methodologies with clear validity and scalability tradeoffs</li>
<li>Analyze model behavior and failure patterns; generate actionable recommendations for model improvement and evaluation redesign</li>
<li>Collaborate with AI/ML Research Engineers to translate research methods into scalable evaluation and post-training pipelines</li>
<li>Collaborate with Language Data Scientists to integrate human-in-the-loop and synthetic data/evaluation strategies into research programs</li>
<li>Engage with customer technical stakeholders to understand evaluation goals, review methodologies, and provide expert recommendations</li>
<li>Contribute to internal benchmark datasets, evaluation frameworks, and reusable research assets</li>
<li>Produce high-quality technical documentation, internal research reports, and client-facing materials explaining methods, results, assumptions, and limitations</li>
<li>Contribute to thought leadership and best practices in LLM evaluation, post-training, and GenAI quality measurement</li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>MS/PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, AI, or a related quantitative scientific field (PhD strongly preferred)</li>
<li>5+ years of relevant experience in applied research / research science in ML/AI, with substantial work in LLMs or foundation models</li>
<li>Demonstrated experience with LLM evaluation, benchmarking, alignment, post-training, or model quality research</li>
<li>Strong foundation in experimental design, statistical analysis, and scientific reasoning for ML systems</li>
<li>Strong coding skills in Python for research experimentation and analysis (e.g., data processing, evaluation pipelines, statistical analysis, visualization)</li>
<li>Experience working with modern ML tooling/frameworks (e.g., PyTorch, Hugging Face, JAX/TensorFlow as applicable) sufficient to design and execute model/evaluation experiments</li>
<li>Ability to evaluate and compare human and automated evaluation methods, including tradeoffs in cost, reliability, validity, and scalability</li>
<li>Experience designing evaluation studies and protocols that are reproducible across datasets, model versions, and evaluation runs</li>
<li>Ability to collaborate directly with technical stakeholders including research scientists, ML engineers, data scientists, and customer technical counterparts</li>
<li>Strong communication skills and ability to present nuanced technical conclusions, assumptions, and limitations clearly</li>
</ul>
The expected salary range for this position is $175,000 – $225,000 USD per year, based on experience, skills, and qualifications.
 <div>
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 <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Innodatainc

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Data Engineer

Applied Research Scientist, LLM Evaluation & Post-Training

Applied Data Scientist, Finance AI Evaluation & Datasets

Applied Research Scientist, LLM Evaluation & Post-Training

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