Computational Scientist, Protein Engineering

Manifold Bio is a dynamic biotech company building a pipeline of targeted biologics using a novel in vivo-centric discovery approach. Our drug discovery engine is differentiated by massively parallel screening in vivo from the beginning of our discovery process. This unique platform is powered by a proprietary protein barcoding technology that allows multiplexed protein quantitation at unprecedented scale and sensitivity. We combine this and other high-throughput protein engineering approaches with computational design to create antibody-like drugs and other biologics.Our world-class team of protein engineers, biologists, and computational scientists are working together to aim the platform at therapeutic opportunities where precise targeting is the key to overcoming clinical challenges.

Manifold Bio is seeking an exceptional Computational Scientist to join our growing Compute team. You will work closely with experimental and hybrid 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 CTO 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

• Support critical data streams including phi-seq, yeast display (Tite-Seq), and other proprietary readouts.
• Build robust data pipelines with rich metrics and statistics for real-time dataset analysis and reporting.
• Perform analysis and "hit calling" support when metrics are less well-defined, contributing new insights to active projects.
• Improve library design workflows to best co-optimize antibody libraries for enhanced performance.
• Write robust code for common protein design tasks including codon optimization, library design, sampling, and molecule selection.
• Collaborate on designing high-throughput experiments and analyze/interpret results from phage display (biopanning, phi-seq, in vivo) and yeast display platforms.
• Create automated solutions and data pipelines to support standardized production-grade studies and democratize data-driven decision making.
• 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
• 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
• Fluency in Python and experience with data pipeline development
• Familiarity with version control, test-driven development, and Unix computing
• Outstanding written and verbal communication skills for cross-functional collaboration

Preferred Qualifications

• 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

We value different experiences and ways of thinking and believe the most talented teams are built by bringing together people of diverse cultures, genders, and backgrounds.