SystImmune is expanding its AI and computational discovery team to identify novel drug targets and design next-generation therapeutics for cancer treatment.
Requirements
- Demonstrated application of ML/AI to therapeutic R&D (e.g., gene expression modeling, target nomination, protein interaction prediction).
- Hands-on proficiency with Python, R, PyTorch or TensorFlow, and related bioinformatics/ML tools.
- Experience with multi-modal data integration, including single-cell, bulk RNA-seq, proteomics, or clinical data.
- Exposure to protein structure modeling or antibody engineering is highly desirable.
- Prior work on T cell engagers, ADC programs, or bispecific antibodies.
- Understanding of protein-ligand interactions, payload selection, or immune checkpoint design.
- Knowledge of tools such as AlphaFold, Rosetta, DiffDock, or protein language models.
Responsibilities
- Develop and apply ML/AI methods to identify and prioritize novel drug targets, including T cell engagers, ADCs, and multispecific antibodies.
- Engineer and optimize therapeutic strategies using ML models, including payload strategies and checkpoint combinations for cancer indications.
- Build scalable and interpretable machine learning models (e.g., DL, VAEs, GNNs) using public and internal multi-omics, structural, and clinical datasets.
- Analyze complex datasets (RNA-seq, proteomics, perturbation, clinical trial data) to generate actionable insights into cancer biology and treatment response.
- Interpret outputs from ML models and guide experimental validation, providing insight into feasibility, mechanistic pathways, and therapeutic relevance.
Other
- 5+ years of industry experience in drug discovery or therapeutic development required.
- Strong experience with drug development platforms, ideally including target selection/validation and biologic modality development (ADC, TCE, antibodies).
- Familiarity with oncology-focused discovery, especially involving immune checkpoints, payload strategies, or tumor-specific targets.
- PhD or Master’s in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Biostatistics, or a related field.
- The opportunity to directly impact first-in-class cancer immunotherapies.
