SystImmune is expanding its AI and computational discovery team to identify novel drug targets and design next-generation therapeutics for cancer treatments.
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
- PhD or Master’s in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Biostatistics, or a related field
- 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
- Work closely with protein engineers, immunologists, and translational scientists to integrate AI-driven hypotheses into the drug pipeline
