Medical Specialist

Overview

Job Title : Generative AI Associate Analyst (LLM Training – Medical Domain)

Location : Remote

Employment Type : Flexible Contract Role (Part-time, up to 25 hours weekly)

Innodata (NASDAQ : INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are an AI technology solutions provider-of-choice for 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine. By combining advanced machine learning and artificial intelligence (ML / AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of AI. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms. Our global workforce includes over 5,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany.

About the Role

We are seeking highly qualified professionals with (Master’s or PhD) in Medical Microbiology to support the development of state-of-the-art Large Language Models (LLMs). As a Rater / AI Trainer, you will play a critical role in shaping how AI understands and communicates complex biomedical knowledge, with a specific focus on microbiological sciences.

This role involves evaluating AI-generated responses for scientific accuracy, contextual integrity, and clinical relevance within the field of medical microbiology. Your feedback and annotations will inform the improvement of AI systems used in healthcare education, diagnostics support, public health communication, and biomedical research.

Key Responsibilities

• Critically assess AI-generated responses in topics including microbial taxonomy, infectious disease mechanisms, host–pathogen interactions, diagnostic microbiology, antimicrobial resistance, and laboratory methodologies.
• Apply structured annotation to texts, including labeling for scientific correctness, terminology accuracy, logical structure, and clinical relevance.
• Categorize model outputs using domain-specific taxonomies (e.g., pathogen type, disease process, diagnostic method).
• Create real-world clinical or laboratory scenarios with researchers to design and test prompts to assess the model’s reasoning and adaptability to professional contexts (e.g., interpreting culture reports, recommending diagnostic tests, understanding resistance profiles).

Required Qualifications

As part of the project, you are required to complete the English language assessment. The assessment is mandatory and non-billable.

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Job Details

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