Scientist, cDNA Display

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 a scientist to build and own a high-throughput, automated cell-free display platform for screening computationally designed protein binder libraries against hundreds of therapeutic receptor targets. This platform will be a cornerstone of Manifold's closed-loop AI-driven molecule discovery engine, generating the large-scale, high-quality binding data needed to train and improve our design models. The ideal candidate is a hands-on scientist with deep expertise in cell-free display technologies and a drive to build automated systems that can operate at scale. You will work at the intersection of protein engineering, laboratory automation, and ML-guided drug discovery, collaborating closely with our binder discovery, ML, and automation teams. Responsibilities Design, develop, and validate a cell-free display platform (cDNA display or similar) compatible with diverse binder formats Work with automation team to scale and automate the platform to enable routine, high-throughput screening against large panels of therapeutic targets Collaborate with ML and computational teams to ensure screening outputs feed effectively into model training pipelines Work with the protein engineering team to support discovery campaigns and affinity maturation as platform capacity allows Stay current on advances in cell-free display, laboratory automation, and computational protein design Required Qualifications PhD or equivalent experience in protein biochemistry, molecular biology, biological engineering, or a related field Hands-on experience with cell-free selection systems (cDNA display, mRNA display, ribosome display, or similar) Strong molecular biology fundamentals and demonstrated ability to develop and troubleshoot complex, multi-step workflows Experience with or strong interest in laboratory automation Preferred Qualifications Track record of successfully automating complex laboratory workflows Background in therapeutic antibody discovery or protein engineering Experience with computational protein design Familiarity with how high-throughput screening data feeds into ML and computational pipelines Experience with complex or non-traditional binder formats and biochemical intuition to adapt selection workflows accordingly Why you might be a good fit Building systems that generate data at a scale previously impossible excites you You thrive in fast-moving environments where you own a problem end-to-end You are energized by working at the intersection of wet lab science, automation, and AI-driven biotherapeutic discovery You are a collaborative scientist who communicates well across teams with different technical backgrounds Base Salary Range: $118,000-138,000 This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact. This role is eligible for: Annual performance-based target bonus Stock options Comprehensive medical, dental, and vision coverage 401(k) plan Flexible paid time off and holidays Perks including on-site gym, onsite lunch, and commuter support Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity. 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.

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