Senior​/Principal Scientist, Molecular Simulations

Position: Senior / Principal Scientist, Molecular Simulations
About the Role:

FL117, a venture-backed stealth AI x bio company, is seeking a Sr. Scientist / Principal Scientist, Molecular Simulations. Here you will build the physics layer of a drug development AI platform. You will characterize how binders engage their targets and validate generated designs before they are committed to synthesis. You will work at the interface of structure-based modeling and generative ML, turning simulation output into training signals.

We are looking for a hands-on simulation scientist who is comfortable owning a method end to end in a cross-functional, fast-moving team. Responsibilities:
Binder Characterization:
Run and interpret structural and dynamic analysis of ligand–target complexes peptides, macrocycles, nanobodies/VHHs, antibody CDRs. Pocket & Target Assessment:
Characterize binding sites, including cryptic and induced-fit pockets, across the target portfolio using MD, enhanced sampling, and pocket detection methods.  Design Validation:
Triage generated molecules before synthesis through a combination of pose prediction and refinement, docking rescoring, binding-mode stability under MD, ligand strain and conformational ensemble analysis, and define the criteria by which a design advances or gets killed.  Free Energy & Affinity Prediction:
Build, validate, and own the affinity prediction workflow (relative and absolute FEP, MM-GBSA/PBSA, endpoint methods); establish its accuracy and applicability domain against measured data and report both honestly.  Beyond-Rule-of-5 Properties:
Model conformational behavior, intramolecular hydrogen bonding, chameleonicity, and membrane partitioning for macrocycles and other non-classical chemo types to support oral property design.  Simulation-to-ML Interface:
Convert simulation output into structured signal for the generative platform in close partnership with the AI/ML team.  Infrastructure & Throughput:
Build reproducible, automated simulation pipelines on GPU cloud or HPC infrastructure; own system setup, parameterization, QC, and analysis at portfolio scale rather than one system at a time.  Cross-functional Partnership:
Work directly with medicinal chemistry, pharmacology, and external structural biology and assay partners; turn structural hypotheses into testable designs, feed returning data back into corrected models, and flag clearly where predictions are unreliable.  Must-have

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, and the characteristic failure modes of each.  

Production experience with at least one enhanced sampling or free energy framework (relative or absolute FEP/TI, meta dynamics, umbrella sampling, replica exchange, or weighted-ensemble MD) including validation against measured affinities.  Structure-based drug design fundamentals: docking and pose evaluation, pharmacophore and hot-spot analysis, and critical assessment of experimental and predicted structures.  Strong Python, with fluency in simulation tool chains (OpenMM, GROMACS, AMBER, NAMD, or Desmond) and analysis stacks (MD Analysis/MDTraj, RDKit);

version control and reproducible workflows as habit, not aspiration.  Demonstrated ability to run simulations at scale on GPU, HPC, or cloud infrastructure through automation rather than manual…