Senior / Principal Scientist, Molecular Simulations

Flagship Pioneering, Inc.•Cambridge, MA

4d•$146,000 - $236,500

About The Position

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.

Requirements

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, metadynamics, 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 toolchains (OpenMM, GROMACS, AMBER, NAMD, or Desmond) and analysis stacks (MDAnalysis/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 per-system setup.
Judgment about method cost versus decision value: able to say when a multi-week free energy campaign is warranted and when docking plus a short MD run is enough.
Consistent record of outstanding technical output reflected in publications, patents, or high impact internal reports

Nice To Haves

Peptide, macrocycle, or other beyond-Rule-of-5 simulation: conformational ensembles, intramolecular hydrogen bonding, chameleonicity, and membrane permeability (ex: water-to-membrane transfer free energies).
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 (ANI, MACE, NequIP-class), learned scoring functions, or generating simulation-derived features for downstream generative models.
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. Please send a link to representative figures or your GitHub, if applicable.
Strong presence in the computational chemistry or biophysics community through publications and conference proceedings. Please send a list of publications, if applicable.

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 chemotypes 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.