Computational Scientist, Protein Engineering

Bostonmid

OtherScientist

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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.

Responsibilities

~1 min read

→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

Requirements

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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

Requirements

~1 min read

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

You thrive in collaborative environments where computational insights directly guide experimental decisions
You're energized by translating complex datasets into actionable recommendations for drug discovery teams
You enjoy building robust, production-quality tools that others rely on for critical decisions
You're passionate about the therapeutic potential of engineered antibodies and want to accelerate their development
You love working at the intersection of cutting-edge computational methods and innovative experimental platforms

What We Offer

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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.