# Senior / Principal Scientist, Molecular Simulations

**Company**: FL117, a venture-backed stealth AI x bio company
**Location**: Cambridge, MA
**Experience**: senior
**Job type**: full-time
**Salary**: $146,000 - $236,500
**Category**: Research
**Industry**: Healthcare

**Apply**: https://job-boards.greenhouse.io/flagshippioneeringinc/jobs/8843622002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_c9d202fd-f63

## Description

FL117, a venture-backed stealth AI x bio company, is seeking a Sr. Scientist / Principal Scientist, Molecular Simulations. 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.

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.

- Free Energy & Affinity Prediction: Build, validate, and own the affinity prediction workflow (relative and absolute FEP, MM-GBSA/PBSA, endpoint methods).

- Beyond-Rule-of-5 Properties: Model conformational behavior, intramolecular hydrogen bonding, chameleonicity, and membrane partitioning for macrocycles and other non-classical chemotypes.

- 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.

- Cross-functional Partnership: Work directly with medicinal chemistry, pharmacology, and external structural biology and assay partners.

Must-have Qualifications:

- PhD in computational chemistry, biophysics, chemical physics, structural biology, or a related field.

- 4+ years of post-PhD molecular simulation experience, at least part of it in a drug discovery setting.

- Deep hands-on expertise in classical molecular dynamics: force fields and small molecule parameterization.

- 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 and analysis stacks.

- Demonstrated ability to run simulations at scale on GPU, HPC, or cloud infrastructure through automation.

Preferred Experience:

- Peptide, macrocycle, or other beyond-Rule-of-5 simulation.

- Experienced with target classes such as: GPCRs, protein–protein interactions, transcription factors.

- Working with predicted structures and their limitations.

- ML-adjacent simulation work: machine-learned force fields and potentials.

- Workflow and infrastructure tooling: Snakemake/Nextflow, Airflow/Prefect/Dagster, Docker, AWS.

- Clear structural communication to non-specialists using PyMOL, ChimeraX, or Mol*.

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.

## Skills

### Required
- molecular simulations
- structure-based modeling
- generative ML
- classical molecular dynamics
- force fields
- small molecule parameterization
- enhanced sampling
- free energy framework
- docking
- pose evaluation
- pharmacophore analysis
- Python
- simulation toolchains
- analysis stacks

### Nice to have
- peptide simulation
- macrocycle simulation
- GPCR simulation
- protein–protein interaction simulation
- transcription factor simulation
- machine-learned force fields
- potentials
- workflow and infrastructure tooling
- clear structural communication

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