Senior / Principal Scientist, Molecular Simulations

The role

You will build the physics layer of a drug development AI platform. You will characterize binder engagement and validate generated designs using molecular simulations.

What you will do

• Run and interpret structural and dynamic analysis of ligand–target complexes
• Characterize binding sites using MD, enhanced sampling, and pocket detection methods
• Triage generated molecules through pose prediction, refinement, and docking rescoring
• Build, validate, and own the affinity prediction workflow
• Model conformational behavior and membrane partitioning for non-classical chemotypes
• Convert simulation output into structured signal for the generative platform
• Build reproducible, automated simulation pipelines on GPU cloud or HPC infrastructure
• Work with medicinal chemistry, pharmacology, and external structural biology partners

Required qualifications

• PhD in computational chemistry, biophysics, chemical physics, structural biology, or a related field; MSc with equivalent industry depth considered
• 4+ years of post-PhD molecular simulation experience, at least part of it in a drug discovery setting (Sr. Scientist)
• 6+ years with demonstrated ownership of a method or platform (Principal)
• Deep hands-on expertise in classical molecular dynamics: force fields and small molecule parameterization (AMBER/CHARMM/OPLS, GAFF/OpenFF), solvation and ion treatment, equilibration protocols
• Production experience with at least one enhanced sampling or free energy framework
• Structure-based drug design fundamentals: docking and pose evaluation, pharmacophore and hot-spot analysis
• Strong Python, with fluency in simulation toolchains (OpenMM, GROMACS, AMBER, NAMD, or Desmond) and analysis stacks (MDAnalysis/MDTraj, RDKit)
• Demonstrated ability to run simulations at scale on GPU, HPC, or cloud infrastructure through automation
• Consistent record of outstanding technical output reflected in publications, patents, or high impact internal reports

Nice to have

• Peptide, macrocycle, or other beyond-Rule-of-5 simulation
• Experienced with target classes such as: GPCRs, protein–protein interactions, transcription factors, highly dynamic target classes
• Working with predicted structures and their limitations (AlphaFold-class models, co-folding methods), and with cryptic or induced-fit pocket discovery
• ML-adjacent simulation work: machine-learned force fields and potentials, learned scoring functions, or generating simulation-derived features
• Workflow and infrastructure tooling: Snakemake/Nextflow, Airflow/Prefect/Dagster, Docker, AWS (Batch, ParallelCluster, S3), and experiment tracking (MLflow/W&B)
• Clear structural communication to non-specialists using PyMOL, ChimeraX, or Mol*

Benefits

• Healthcare coverage
• Annual incentive program
• Retirement benefits
• Broad range of other benefits

About the company

FL117 is a venture-backed stealth AI x bio company. Flagship Pioneering invents and builds platform companies with the potential to transform human health, sustainability and beyond.