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Job Title:
Biological & Biomedical Sciences / Chemistry Subject Matter Expert – AI Training & Content Development
About Innodata:
Innodata (NASDAQ: INOD) is a global leader in data engineering and AI-powered technology solutions, trusted by 2,000+ enterprise customers worldwide, including 4 of the top 5 global technology companies. With operations in 13 cities and a workforce of over 5,000 experts across the United States, Canada, the UK, the Philippines, India, Sri Lanka, Israel, and Germany, Innodata is at the forefront of artificial intelligence (AI), natural language processing (NLP), and large language model (LLM) innovation.
We combine cutting-edge ML/AI technologies, deep scientific expertise, and high-security infrastructure to deliver next-generation solutions for industries spanning pharma, life sciences, healthcare, technology, finance, and law.
About the Role:
We are seeking Subject Matter Experts (SMEs) in Biological & Biomedical Sciences and Chemistry to join our AI/ML model training and content development team. In this role, you will leverage your scientific expertise to curate, validate, and create domain-specific content that improves AI reasoning, knowledge representation, and performance in life sciences and chemical sciences.
You will directly contribute to the training of advanced LLMs and scientific AI systems by developing datasets, reviewing model outputs, and ensuring alignment with scientific accuracy, regulatory compliance, and ethical standards.
Key Responsibilities:
Content Creation & Knowledge Engineering:
- Develop scientifically accurate, domain-rich datasets in:
- 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 for accuracy, reproducibility, and compliance with scientific and safety standards.
- Identify and correct conceptual errors, dataset biases, or incomplete knowledge representations.
- Contribute to knowledge graph development and ontology engineering for biological and chemical datasets.
Cross-Disciplinary Collaboration:
- Collaborate with AI engineers, data scientists, and computational chemists to optimize model reasoning.
- Support the creation of adversarial testing datasets for LLMs in biomedical and chemical domains.
- Participate in R&D discussions to enhance AI performance in drug discovery, materials science, and biomedical research applications.
Eligibility Criteria:
- Advanced degree required: Master’s, PhD, or equivalent in Biology, Biomedical Sciences, Chemistry, Biochemistry, Molecular Biology, Chemical Engineering, or a related field.
- Strong expertise in laboratory research, computational modeling, bioinformatics, or chemical analysis.
- Proven experience in scientific writing, peer-reviewed publication, or technical documentation.
- Familiarity with regulatory 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 an English Language Assessment.
- Mandatory and non-billable step.
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