Computational Scientist, Assay Development

Manifold Bio is a platform biotechnology company pioneering AI-guided protein design and massively multiplexed in vivo screening to unlock tissue-targeted medicines and organism-scale models of living systems. Using proprietary molecular barcoding technology, we screen hundreds of thousands of protein designs simultaneously in living systems, producing in vivo-validated datasets at a scale no one else can match. The datasets power our computational models, which leads to better drug designs, creating a flywheel that gets stronger with every campaign. Our team of protein engineers, biologists, and computational scientists works across this full stack to pursue programs both internally and with leading pharma companies. Position Manifold Bio is seeking an exceptional Computational Scientist to join our growing Quantitative Biology team. You will work closely with experimental scientists to design and analyze highly multiplexed protein library experiments. You will specifically be working with traditional phage and yeast display readouts, as well as other proprietary display data. You will be expected to own and independently advance projects in areas related to your deep expertise such as protein design, DNA library design, or machine learning/biophysical modeling from MPRA data. You will work closely with our Head of Platform and our other computational scientists to onboard new capabilities that advance the M-Design platform for data-driven engineering of drugs with desired properties. Responsibilities Invent new quantitative protein engineering assays working with experimental scientists Build robust data pipelines with rich metrics and statistics for real-time dataset analysis and reporting Perform analysis and "hit calling" support, contributing new insights to active projects Improve library design workflows to best co-optimize antibody libraries for enhanced performance Write and contribute robust code in shared libraries for common protein design tasks Collaborate on designing high-throughput experiments and analyze/interpret results from phage display (biopanning, phi-seq, in vivo) and yeast display platforms Deliver high-quality data reporting through slides and documentation for cross-functional teams Proactively share findings with colleagues through excellent documentation and discussions Required Qualifications PhD and/or 4+ years of equivalent experience in computational biology, bioinformatics, protein engineering, antibody engineering, or similar field working with biological sequences Rich experience with Python, agentic coding, data pipeline development Familiarity with version control, test-driven development, and Unix computing Strong antibody engineering background with first-hand experience in antibody design, optimization, or discovery Experience with massively parallel reporter assays (MPRAs) and high-throughput screening data analysis Experience designing DNA libraries for binders, DMS, phage display, or equivalent high-throughput experiments Experience working with Next Generation Sequencing (NGS) data from library-based experiments Strong understanding of statistics fundamentals and data analysis methodologies Outstanding written and verbal communication skills for cross-functional collaboration Preferred Qualifications Intuition or exposure to best practices in software/data engineering Industry experience in antibody therapeutic development or biotechnology R&D Track record of developing computational tools or pipelines adopted by experimental teams Experience mentoring junior scientists or leading cross-functional project teams Publications or patents in antibody engineering, protein design, or high-throughput screening methods Familiarity with cloud computing platforms (AWS, GCP) and containerization technologies This Role Might Be Perfect For You If You thrive in collaborative environments where computational insights directly guide experimental decisions You're energized by translating complex d