Medical Specialist

4 semanas atrás


Campinas, Brasil Innodata Inc. Tempo inteiro

Job Details Job Title : Generative AI Associate Analyst (LLM Training – Medical Domain) Location : Remote Employment Type : Flexible Contract Role (Part-time, up to 25 hours weekly) Overview We are seeking highly qualified professionals with a Master’s or PhD in Medical Microbiology to support the development of state‑of‑the‑art Large Language Models (LLMs). As an AI Trainer, you will evaluate AI‑generated responses for scientific accuracy, contextual integrity, and clinical relevance within medical microbiology. Your feedback 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 labelling 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 Master’s or PhD in Medical Microbiology, Immunology, or a closely related biomedical discipline. Demonstrated expertise in core areas such as microbial pathogenesis, clinical diagnostics, virology, bacteriology, parasitology, and immunology. Familiarity with laboratory procedures, microbiological instrumentation, and interpretation of diagnostic test results (e.g., blood cultures, PCR, AST). Excellent written communication skills and fluency in English. Required to complete an English language assessment as part of the project. #J-18808-Ljbffr


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