Spearhead our molecular design and simulation initiatives.
Support and accelerate our drug discovery and development pipeline of protein-based therapeutics.
Collaborate with a passionate team of scientists and engineers.
Build, evaluate, refine, and apply sophisticated computational approaches and infrastructures.
Drive the discovery of complex molecules and expedite the development of protein therapeutics across various modalities and indications.
Work closely with colleagues to design, develop and deliver therapeutic molecules for a range of clinical indications.
Work directly with wet-lab scientists to design and optimize protein molecules for specific functions and properties.
Collaborate across Therapeutics Discovery on the planning, prioritization, and delivery of designs for multiple concurrent programs.
Serve as an In Silico Discovery Lead on diverse project teams.
Model sophisticated biological systems to advise design methodology.
Participate in internal and external presentations, peer-reviewed manuscripts and conference proceedings.

Requirements

PhD in Biomedical Engineering, Biochemistry, Computational Biology, Computer Science, Structural Biology, Biophysics, Material Science, or other related quantitative field required.
Advanced experience in computational protein modeling, design, structural biology, and biophysics is required.
Ability to lead projects in cross-functional teams to completion in a collaborative manner is required.
Experience in de novo protein design and sophisticated protein design techniques, such as large scale backbone editing is preferred.
Experience with antibody and/or nanobody modeling and design is required.
Experience with high performance computing (on-prem or cloud) is required.
Knowledge of critical wet-lab techniques used in protein engineering such as yeast display, SPR, ITC, CD-spectroscopy is preferred.
Solid understanding of current machine learning architectures is required.
Advanced proficiency in Python and related data-science packages such as Pandas and Numpy is required.
Ideal candidate is self-motivated, proactive, and independent.
Experience using Schrodinger, Discovery Studio, MOE, GROMACS, NAMD or other molecular dynamics simulation software is preferred.
Experience with powerful ML and biophysics packages for protein modeling and design such as AlphaFold, Boltz2 , ESMFold, ImmuneBuilder, ProteinMPNN, RFDiffusion, RFAntibody, Boltzgen, Germinal, Chai-2, Chroma, Rosetta, etc is required.
Experience with retraining, fine-tuning, and building ML models such as LLMs to support advanced protein design and protein property prediction tools is preferred.
Experience mentoring and leading junior scientists is strongly preferred.
Desire for continuous learning and the ability to identify, evaluate and deploy emerging algorithms, models, and protein design workflows is required.

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

computational protein modelingprotein designstructural biologybiophysicsde novo protein designbackbone editinghigh performance computingmachine learning architecturesPythondata-science packages

Soft Skills

leadershipcollaborationself-motivatedproactiveindependentmentoringcontinuous learningproject management

Certifications

PhD in Biomedical EngineeringPhD in BiochemistryPhD in Computational BiologyPhD in Computer SciencePhD in Structural BiologyPhD in BiophysicsPhD in Material Science