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Proclinical Logo Proclinical · Research and Development

Research Scientist, AI/ Machine Learning

United States, Cambridge · Job Posted March 22, 2026
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Job Description

Seeking an AI‑driven Research Scientist to push the boundaries of immuno‑oncology and next‑gen biologic design. Proclinical is seeking a Research Scientist specializing in AI and Machine Learning to support innovative advancements in immuno-oncology and pharmaceutical research.

Job Responsibility

  • Design and implement advanced AI/ML approaches for antibody discovery, including fine-tuning protein language models and generative protein design workflows
  • Develop scalable machine learning methods for multi-objective optimization of biologics such as antibodies, antigens, ADCs, and other modalities
  • Build sequence-aware predictive models to prioritize ASO designs based on exon-skipping responses across diverse targets and modalities
  • Create reproducible computational frameworks for biologics, encompassing data ingestion, feature engineering, model training, validation, and deployment
  • Curate and harmonize datasets, defining robust sequence and structure features to drive model performance
  • Establish benchmarks and collaborate with experimental teams to validate predictions
  • Evaluate and adopt tools to enhance modeling workflows and decision support systems
  • Maintain a clean, well-documented codebase and provide user guidance for cross-functional teams
  • Perform additional related tasks as assigned

Requirements

  • PhD in Computational Chemistry/Biology, Machine Learning, Biomedical/Chemical Engineering, or a related field
  • Strong background in oligonucleotide chemistry and antibody design
  • Proven experience in computational modeling of antibody-antigen interactions
  • Expertise in probabilistic learning, deep learning models (e.g., RNNs, GNNs, Transformers), and generative AI
  • Proficiency in programming languages such as Python, R, and SQL, with hands-on experience in frameworks like PyTorch, TensorFlow, or JAX
  • Experience developing machine learning models for DNA, RNA, and proteins, including language models and structure prediction
  • Familiarity with large-scale computing, cloud infrastructures, and database systems
  • Knowledge of tools like AWS, GitHub/GitLab, and Docker containers
  • Strong communication skills to collaborate effectively with multidisciplinary teams
  • Commitment to teamwork and continuous learning
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