Job Title

Innodata (NASDAQ: INOD) is a leading data engineering company serving over 2,000 customers worldwide. We are the AI solutions provider of choice for four of the five largest global technology companies, as well as top-tier organizations in finance, insurance, law, healthcare, and more. With a global workforce of over 5,000 employees and presence in 13 cities across the US, Canada, UK, Germany, Israel, India, Sri Lanka, and the Philippines, we combine advanced ML/AI technologies, subject matter expertise, and secure infrastructure to unlock the full potential of artificial intelligence. About the Role: We are seeking highly analytical and detail-oriented scientific experts to support our AI training and model evaluation initiatives. This role is ideal for PhDs in Medical Sciences, Biomedical Sciences, Life Sciences, or related disciplines who have a passion for research, critical thinking, and applying domain-specific knowledge to cutting-edge AI applications. You will contribute to the development and improvement of AI systems, including large language models (LLMs) and other machine learning pipelines, by creating, curating, and evaluating medical and scientific datasets, validating model outputs, and providing domain-specific insights. Key Responsibilities: Create, review, or annotate high-quality medical and scientific content and datasets to train or evaluate AI systems. Perform quality assurance on model-generated outputs for clinical and scientific accuracy, clarity, and alignment with domain knowledge. Analyze and interpret AI behavior in the context of medical and biomedical tasks and error patterns. Support the development of guidelines for medical content generation and annotation. Collaborate with internal engineering, data, and linguistic teams to ensure accuracy and consistency across projects. Conduct domain-specific research and synthesize findings to guide model improvements. Identify and resolve issues related to ambiguity, bias, or misrepresentation in medical and scientific content. Qualifications: PhD in Medical Sciences, Biomedical Sciences, Life Sciences, or a closely related discipline. Strong analytical skills and ability to apply medical and scientific knowledge to real-world datasets and AI systems. Familiarity with clinical research standards, peer-reviewed publishing, or lab-based research methodologies. Attention to detail and ability to critically evaluate medical/scientific content for accuracy and clarity. Excellent writing, editing, and communication skills. (Preferred) Experience with AI/ML concepts, data annotation, programming, or biomedical informatics. Nice to Have: Experience working with large medical or scientific datasets. Knowledge of machine learning pipelines, NLP, or LLMs. Previous experience in interdisciplinary research, clinical research, or healthcare consulting.