Flagship Pioneering
Senior / Principal Scientist, Molecular Simulations
$146K–$236K
Cambridge, MA USA +1 · Principal · Full-time · Posted Oct 1, 2026
- Location
- Cambridge, MA USA
FL117 - Experience
- 4+ years post-PhD for Sr. Scientist, with at least some in drug discovery; 6+ years for Principal Scientist, with demonstrated ownership of a method or platform.
- Education
- PhD in computational chemistry, biophysics, chemical physics, structural biology, or a related field; MSc with equivalent industry depth considered.
About the job
Build and own molecular simulation workflows that characterize and validate drug designs, predict binding and properties, and provide structured signals to a generative AI drug development platform.
What you'll do
- Analyze binder–target interactions and binding pockets using molecular dynamics and related methods.
- Validate generated designs and build affinity prediction workflows, assessing performance against experimental data.
- Automate simulation pipelines at scale and translate simulation results into useful signals for the AI/ML team.
What they ask for
- PhD in a relevant field; MSc with equivalent industry depth considered.
- 4+ years of post-PhD molecular simulation experience, including some drug discovery experience, for Sr. Scientist; 6+ years with demonstrated method or platform ownership for Principal Scientist.
- Hands-on expertise in classical molecular dynamics, force fields and small-molecule parameterization; production experience with enhanced sampling or free-energy methods validated against measured affinities.
- Strong Python and simulation-toolchain experience, plus demonstrated automated simulation work on GPU, HPC, or cloud infrastructure.
Nice to have
- Simulation experience with peptides, macrocycles, or other beyond-Rule-of-5 molecules.
- Experience with predicted structures, cryptic or induced-fit pocket discovery, or ML-adjacent simulation work.
Pay
$146,000 - $236,500 USD (base salary)
Tools
AMBER, CHARMM, OPLS, GAFF/OpenFF, OpenMM, GROMACS, Python, RDKit
AI focus
Convert molecular simulation output into structured signals for a generative drug development platform; preferred experience includes machine-learned force fields, learned scoring functions, or simulation-derived features for generative models.
Model-written summary · the original posting is authoritative