Machine Learning Scientist​/Sr. Scientist - Drug Target Discovery

Syst Immune is a leading and well-funded clinical-stage biopharmaceutical company located in Redmond, WA and Princeton, NJ. It specializes in developing innovative cancer treatments using its established drug development platforms, focusing on bi-specific, multi-specific antibodies, and antibody-drug conjugates (ADCs). Syst Immune has multiple assets in various stages of clinical trials for solid tumor and hematologic indications. Alongside ongoing clinical trials, Syst Immune has a robust preclinical pipeline of potential cancer therapeutics in the discovery or IND-enabling stages, representing cutting-edge biologics development.

We offer an opportunity for you to learn and grow while making significant contributions to the company’s success.

With a growing pipeline and multiple clinical programs in solid tumors and hematologic malignancies, we are expanding our AI and computational discovery team to identify novel drug targets and design next-generation therapeutics. We are seeking a Machine Learning Frontier Scientist/Sr. Scientist with a proven track record applying AI to drug development, specifically in areas like target discovery, antibody or ADC engineering, and cancer immunotherapy.

This is not a data management role. The ideal candidate will bring domain-specific expertise in oncology, immunology, or protein therapeutics and be comfortable operating at the frontier of ML applications in therapeutic design.

• Drug Target Discovery:
  Develop and apply ML/AI methods to identify and prioritize novel drug targets, including T cell engagers, ADCs, and multispecific antibodies.
• Therapeutic Design:
  Engineer and optimize therapeutic strategies using ML models, including payload strategies and checkpoint combinations for cancer indications.
• Model Development:
  Build scalable and interpretable machine learning models (e.g., DL, VAEs, GNNs) using public and internal multi-omics, structural, and clinical datasets.
• Data Mining for Oncology:
  Analyze complex datasets (RNA-seq, proteomics, perturbation, clinical trial data) to generate actionable insights into cancer biology and treatment response.
• Cross-functional Collaboration:
  
  Work closely with protein engineers, immunologists, and translational scientists to integrate AI-driven hypotheses into the drug pipeline.
• Scientific Feedback Loop:
  Interpret outputs from ML models and guide experimental validation, providing insight into feasibility, mechanistic pathways, and therapeutic relevance.

• 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).
• Demonstrated application of ML/AI to therapeutic R&D (e.g., gene expression modeling, target nomination, protein interaction prediction).
• Familiarity with oncology-focused discovery, especially involving immune checkpoints, payload strategies, or tumor-specific targets.
• Hands‑on proficiency with Python, R, PyTorch or Tensor Flow, and related bioinformatics/ML tools.
• Exposure to protein structure modeling or antibody engineering is highly desirable.
• Experience with multi-modal data integration, including single-cell, bulk RNA‑seq, proteomics, or clinical data.

• 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 Alpha Fold, Rosetta, Diff Dock, or protein language models.
• Experience working with drug development platforms across cancer and other disease areas.

• Generalist data scientists or clinical data managers without direct therapeutic development experience.
• Candidates lacking domain exposure to biologics, oncology, or drug platform work.

• The opportunity to directly impact first-in-class cancer immunotherapies.
• A collaborative and fast-moving environment bridging biology, engineering, and AI.
• Acce…