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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Expertise in designing and training foundation models for drug discovery, with a strong background in computational biology and deep learning. Proficient in molecular dynamics simulations and modern machine learning techniques, complemented by effective communication and collaboration skills.
Highest-signal resume keywords
PhD In Computational BiologyPython ProgrammingDeep Learning Libraries (PyTorch, JAX)Molecular Dynamics SimulationsMolecular Modeling Tools (OpenMM, Rosetta)
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Deep Learning Model ArchitectureMultimodal Representation LearningGeometric Deep LearningDiffusion ModelsClassical Force Fields (AMBER, CHARMM, OpenFF)
Soft Skills
Strong Communication SkillsCollaboration Skills
Tools & Technologies
OpenMMRosetta
Industry Keywords
Drug DiscoveryProtein EngineeringComputational SciencesResearch Publications
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- Design and train foundation models at scale for large-molecule drug discovery and protein engineering
- Leverage structural biology and biophysical datasets
- Build novel architectures capturing complex geometric and physical priors
- Contribute to cross-functional research teams across the Computational Sciences Center of Excellence
- Contribute to and drive publications
- Present scientific findings at internal and external venues
- Solve pressing drug discovery problems that enable new portfolio capabilities
Requirements
What you’ll need- PhD degree in Computational Biology, Computer Science, Chemistry, Physics or related disciplines
- Up to 2 years of industry research experience for Scientist or 2+ years of industry research experience for Senior Scientist
- Demonstrated experience with Python and deep learning libraries such as PyTorch and/or JAX
- Demonstrated experience architecting and training deep learning models
- Experience with modern approaches such as multimodal representation learning, geometric deep learning, and diffusion models
- Expertise in molecular dynamics simulations and classical force fields such as AMBER, CHARMM, and OpenFF
- Hands-on experience with molecular modeling tools such as OpenMM and Rosetta
- Demonstrated research experience, including at least one first author publication or equivalent
- Strong communication and collaboration skills
- Public portfolio of computational projects, such as GitHub
- Relocation benefits are not available for this job posting
Benefits
Comp & perks- Discretionary annual bonus may be available based on individual and Company performance
- Benefits detailed at the link provided below
- Equal opportunity employment practices
- Disability accommodations for the online application process