Senior / Principal Scientist, Molecular Simulations

Flagship Pioneering, Inc.; Cambridge (MA); On-site; USD 146,000 - 237,000; Full time

Benefits offered by this job

Healthcare coverage

Annual incentive program

Retirement benefits

Job summary

Flagship Pioneering, Inc. in Cambridge, MA, seeks a Sr. Scientist/Principal Scientist for Molecular Simulations to build the physics layer of a drug design AI platform.

You will own end‑to‑end simulation workflows that connect structure-based modeling with generative ML, turning outputs into training signals for the platform. You will lead ligand–target characterization, binding-pocket assessment, and validation of generated designs before synthesis, with extensive use of MD, free energy

Qualifications

• PhD or MSc with strong research in computational chemistry or related field.
• Post-PhD molecular simulation experience in drug discovery is preferred.
• Experience running MD simulations at scale on GPUs or HPC.

Responsibilities

• Characterize ligand–target complexes using structural and dynamic analysis.
• Assess binding pockets with MD, enhanced sampling, and pocket detection.

Skills

Molecular dynamics

Python

OpenMM

GROMACS

NAMD

MDAnalysis

MDTraj

Education

PhD in computational chemistry/biophysics

MSc with industry depth considered

Tools

AMBER/CHARMM/OPLS

GAFF/OpenFF

solvation & ions

Job description

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 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.

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, 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.

Preferred Experience

• 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*.

About Flagship Pioneering

Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.

Flagship has been recognized twice on FORTUNE's "Change the World" list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company's annual list of the World's Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.

We are an equal opportunity employer

All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, we are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship.

• The salary range for this role is $146,000 - $236,500.
• FL117 currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits.

Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience.