Computational Biology AI/ML Scientist – RNA & Protein Design

Must Haves:
• PhD in Computational Biology, Computational Chemistry, Machine Learning, Bioengineering, Chemical Engineering, or a related field, with 1–2 years of relevant industry or postdoctoral experience
• Must have knowledge in: One of the following: RNA, Oligonucleotide, Peptide, or Antibody design.
• Hands-on experience building deep learning, generative, or agentic AI models for nucleic acid and/or protein design
• Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX, TensorFlow, scikit-learn), with working knowledge of R and SQL
• Strong working knowledge of oligonucleotide design principles, such as hybridization thermodynamics, target accessibility, and RNA secondary structure
Job Description
In this role, you will develop sequence-aware predictive models that prioritize oligonucleotide therapeutics by exon-skipping response, integrating sequence, thermodynamic, and pre-mRNA structural features, and extend these approaches across PMOs, gapmers, siRNA–PMO hybrids, and conjugate designs. You will also contribute to AI/ML-driven de novo design and multi-objective optimization of antibodies, peptides, and other biologics, including modeling of protein structure and antibody–antigen interactions. Working closely with chemists, biologists, and data scientists, you will benchmark and prospectively validate model predictions, helping turn computational insights into experimentally tested designs and expanding the client's cross-modality design and decision-support capabilities.
Requirements:
• PhD in Computational Biology, Computational Chemistry, Machine Learning, Bioengineering, Chemical Engineering, or a related field, with 1–2 years of relevant industry or postdoctoral experience
• Hands-on experience building deep learning, generative, or agentic AI models for nucleic acid and/or protein design
• Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX, TensorFlow, scikit-learn), with working knowledge of R and SQL
• Strong working knowledge of oligonucleotide design principles, such as hybridization thermodynamics, target accessibility, and RNA secondary structure
• Experience modeling exon-skipping efficiency or ASO/siRNA potency from sequence and pre-mRNA structural features
• Experience with protein and RNA language models (e.g., ESM, RNA-FM), structure prediction tools (e.g., AlphaFold, RoseTTAFold), and molecular modeling platforms (e.g., Schrödinger, MOE)
• Exposure to computational modeling of antibody–antigen sequence, structure, and binding interactions
• Familiarity with uncertainty quantification or active learning to guide experimental design
• Experience with reproducible development and deployment practices, including Git (GitHub/GitLab), Docker, database systems, and cloud computing (AWS preferred)
• Strong communication and collaboration skills across multidisciplinary teams of chemists, biologists, and data scientists, with a commitment to tackling challenges and continuous learning