Computational Chemistry Lead – Molecular Identification

Computational Chemistry Lead – Molecular Identification

At Isospec, we’re building a new standard for molecular identification to unlock patient-centric drug development and diagnostics. You’ll own the computational workflows that turn candidate molecules into high-quality IR spectra—fast, reproducibly, and at scale—enabling direct comparison to experimental data and confident molecular .

Build and operate scalable pipelines for in-silico IR spectra generation and experimental validation. Within 6–12 months, success means having an automated and validated pipeline running at scale for in-silico IR spectra generation. Standardized workflows for predicting and comparing spectra should be consistently applied across projects, enabling fast turnaround times of 0.5 to 1 day for theoretical spectrum generation. These reproducible workflows will be fully integrated with both experimental and data science teams.

• Deliver spectra at scale: Run quantum chemistry and molecular dynamics calculations to generate reproducible in-silico IR spectra.
• Pipeline builder: Design, implement, and maintain automated workflows for structure optimization, vibrational analysis, and IR spectrum prediction.
• QC methods: Apply and benchmark state-of-the-art computational approaches (DFT, molecular dynamics, anharmonic corrections, and related methods) to ensure accuracy and reliability.
• Infrastructure: Set up and manage the computational environment—HPC, cloud, containers, and schedulers—to enable high-throughput simulations.
• Automation: Build reproducible pipelines using Python/bash scripting and workflow automation (e.g. ASE, Fire Works, Snakemake, or equivalent).
• QC integration: Validate computational predictions against experimental IR data, iteratively refining methods and protocols.
• Collaboration: Work closely with experimental teams to align computational outputs with laboratory results and define standardized molecular .

• PhD in Computational Chemistry, Theoretical Chemistry, or related field.
• 3–5+ years of hands-on experience with quantum chemistry and molecular dynamics packages (Gaussian, ORCA, CP2K, VASP, GROMACS, or equivalents).
• Strong track record applying DFT and MD to vibrational spectroscopy or related molecular property prediction
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• Proven ability to build and run scalable computational pipelines, ideally with HPC or cloud deployment.
• Solid scripting/programming skills (Python, bash); familiarity with libraries like ASE or MDAnalysis is a plus.
• Independence and ownership: able to take broad goals and translate them into robust, automated solutions.
• Clear communicator who can bridge computational and experimental perspectives.

• Experience automating high-throughput QC calculations.
• Familiarity with containerization (Docker/Singularity) and HPC/cloud-native workflows.
• Exposure to cheminformatics tools and spectral databases.
• Experience in a startup or fast-paced industry environment.

• Impact that matters:
  Help solve one of the most difficult bottlenecks in translational science, trustworthy molecular  actionable biomarkers.
• Build the blueprint:
  Your systems and standards will define how next-gen metabolomics operates at scale.
• Elite collaborators:
  Work with leading hospitals, biotech, and pharma on programs that change patient outcomes.
• Ownership & growth:
  High autonomy, rapid learning, and a direct line from your work to patient benefit.