Partex.AI Technology Pvt. Ltd.

Role & responsibilities As our Immunology SME, you will be expected to: Provide Strategic Guidance: Serve as the primary advisor to our computational and AI teams on all aspects of adaptive immunity and immunogenicity. Guide Model Development: Translate complex biological concepts (e.g., antigen processing, T/B cell activation, tolerance) into clear, actionable requirements for our data scientists. Validate Training Data: Advise on the curation, annotation, and validation of training datasets (e.g., T/B cell epitope libraries) to ensure biological relevance and data quality. Advise on Feature Engineering: Provide expert input on the key biological features (e.g., PTMs, aggregation factors, HLA binding promiscuity) that are critical for predicting immunogenicity. Critically Review Model Outputs: Act as the ultimate "sense check" for our model's predictions. You will be responsible for identifying when outputs contradict established immunological principles and for suggesting improvements. Conduct Immunogenicity Risk Assessments: Apply your expertise to review in silico data and provide strategic risk assessments for novel therapeutic protein candidates. Preferred candidate profile We are looking for a true expert with a deep and nuanced understanding of immunology. Essential Domain Expertise: PhD in Immunology, Molecular Immunology, or a related field (postdoctoral experience highly preferred). Extensive experience (ideally 10+ years) in adaptive immunity , with deep knowledge of: T-cell and B-cell biology Antigen presentation (MHC Class I & II) Immunological tolerance (central and peripheral) TCR and BCR recognition Proven SME in Protein Immunogenicity , specifically for biologics (mAbs, bispecifics, ADCs, fusion proteins, etc.). Deep knowledge of Anti-Drug Antibody (ADA) development mechanisms and the factors that influence immunogenicity (e.g., aggregation, impurities, PTMs). Expertise in T-Cell & B-Cell Epitope Biology , including conformational vs. linear epitopes, HLA promiscuity, and population coverage. Computational & Collaborative Skills: You are not a coder , but you must understand the concepts of AI/ML and computational biology. Familiarity with the principles of in silico epitope prediction tools (e.g., NetMHCpan) and their limitations. Experience (or strong understanding) of what is required to train and validate a model (e.g., training/test sets, validation metrics). Exceptional communication skills with a proven ability to collaborate effectively with data scientists and translate complex biology into computational language.