Job Title:
Biological & Biomedical Sciences / Chemistry Subject Matter Expert – AI Training & Content Development
About Innodata:
Innodata (NASDAQ: INOD) is aglobal leader in data engineering and AI-powered technology solutions, trusted by2,000+ enterprise customersworldwide, including4 of the top 5 global technology companies.
With operations in13 citiesand a workforce of over5,000 expertsacross theUnited States, Canada, the UK, the Philippines, India, Sri Lanka, Israel, and Germany, Innodata is at the forefront ofartificial intelligence (AI), natural language processing (NLP), and large language model (LLM) innovation.
We combinecutting-edge ML/AI technologies,deep scientific expertise, andhigh-security infrastructureto deliver next-generation solutions for industries spanningpharma, life sciences, healthcare, technology, finance, and law.
About the Role:
We are seekingSubject Matter Experts (SMEs)inBiological & Biomedical SciencesandChemistryto join ourAI/ML model training and content development team.In this role, you will leverage your scientific expertise tocurate, validate, and create domain-specific contentthat improves AI reasoning, knowledge representation, and performance in life sciences and chemical sciences.
You will directly contribute to the training of advancedLLMsandscientific AI systemsby developing datasets, reviewing model outputs, and ensuring alignment withscientific accuracy, regulatory compliance, and ethical standards.
Key Responsibilities:
Content Creation & Knowledge Engineering:
Developscientifically accurate, domain-rich datasetsin:
Molecular Biology, Biochemistry, and Genetics
Pharmacology and Biomedical Engineering
Chemistry (Organic, Inorganic, Physical, Analytical, Computational)
Cell and Developmental Biology, Immunology, Microbiology
Biotechnology, Toxicology, and Chemical Engineering
Produce high-quality scientific content, including:
Research abstracts and technical white papers
Experimental design summaries
Standard operating procedures (SOPs)
Analytical chemistry workflows and chemical reaction mechanisms
Biomedical case studies, drug development datasets, and molecular pathway analyses
AI Model Evaluation & Data Refinement:
Validate AI-generated outputs foraccuracy, reproducibility, and compliancewith scientific and safety standards.
Identify and correct conceptual errors, dataset biases, or incomplete knowledge representations.
Contribute toknowledge graph development and ontology engineeringfor biological and chemical datasets.
Cross-Disciplinary Collaboration:
Collaborate with AI engineers, data scientists, and computational chemists to optimize model reasoning.
Support the creation ofadversarial testing datasetsfor LLMs in biomedical and chemical domains.
Participate in R&D discussions to enhance AI performance indrug discovery, materials science, and biomedical research applications.
Eligibility Criteria:
Advanced degree required: Master's, PhD, or equivalent inBiology, Biomedical Sciences, Chemistry, Biochemistry, Molecular Biology, Chemical Engineering, or a related field.
Strong expertise inlaboratory research, computational modeling, bioinformatics, or chemical analysis.
Proven experience inscientific writing, peer-reviewed publication, or technical documentation.
Familiarity withregulatory frameworks, laboratory safety, and ethical research guidelines.
Preferred experience with:
Computational chemistry, cheminformatics, or molecular modeling software.
Bioinformatics pipelines, omics data analysis, and high-throughput screening methodologies.
Additional Information:
Selected candidates must complete anEnglish Language Assessment.
Mandatory and non-billable step.