Sr or Pri Scientist, Neoantigen Engineering

Responsibilities

1.Develop, benchmark, and maintain neoantigen prediction pipelines covering mutation calling, HLA typing, MHC-I/II epitope prediction, and immunogenicity prioritization for personalized tumor vaccine programs;

2.Push beyond classical binding-prediction tools by integrating structure-aware and multimodal immunogenicity models;

3.Integrate multi-omics data (WES, RNA-seq, and beyond) to identify and rank high-confidence neoantigen candidates, and feed results into vaccine design decisions;

4.Design and evaluate multi-epitope vaccine constructs, including epitope selection and ordering, linker design, junction (neo)epitope risk assessment, and sequence-level developability checks;

5.Apply and adapt machine learning approaches (ranking models, classifiers, and modern architectures) to immunogenomic prediction problems;

6.Collaborate closely with translational medicine, clinical, and platform teams to translate computational predictions into experimental designs and interpret validation data;

7.Communicate findings, model performance, and design rationale clearly to computational and experimental stakeholders, including cross-functional and leadership audiences;

8.(Principal level) Set technical strategy for neoantigen engineering, mentor junior scientists, and represent the computational function in cross-functional program planning.

Requirements

1.PhD in Computational Biology, Bioinformatics, Immunoinformatics, Machine Learning, or a related field;

2.3+ years (Senior) or 5+ years (Principal) of hands-on experience in neoantigen prediction, tumor immunogenomics, or closely related computational immunology in an academic or industry setting;

3.Deep familiarity with MHC-I/II epitope prediction and immunogenicity assessment tools; experience with structure-aware immunogenicity models is a strong plus;

4.Solid experience processing and analyzing next-generation sequencing data (WES/RNA-seq variant calling and HLA typing);

5.Strong programming skills in Python and/or R; practical experience applying machine learning to biological data;

6.Familiarity with cloud computing and high-performance computing environments;

7.Excellent written and verbal communication skills; ability to explain computational concepts to non-computational colleagues;

8.Publication record and/or patents in neoantigen prediction, immunoinformatics, or computational oncology is a plus.