Research Engineer — AI-Accelerated Quantum Chemistry

Dayhoff Labs; Cambridge (MA); On-site; USD 140,000 - 200,000; Full time

Benefits offered by this job

Visa sponsorship

Competitive compensation

Job summary

Dayhoff Labs seeks a highly skilled computational chemist to push forward AI-accelerated molecular simulations. You will run QM/MD workflows, build reproducible pipelines across QM, MD, and ML, and deploy tools for internal teams while coordinating with software and ML groups.

Ideal candidates have a PhD or Masters with extensive experience, proficiency in Python and major QM/MD packages, and a track record implementing neural network potentials in chemistry workflows.

Qualifications

• PhD in computational chemistry or related field, or a Master + 3+ years experience.
• Hands-on with QM/MD packages (VASP, Gaussian, ORCA, GROMACS, LAMMPS, CP2K).
• Track record building computational pipelines and reproducible workflows.
• Proficiency in Python and NumPy/SciPy for scientific computing.
• Experience with ML frameworks (PyTorch, TensorFlow) integrated into chemistry workflows.

Responsibilities

• Run QM/MD simulations combining standard packages with AI-accelerated models.
• Build reproducible pipelines and benchmarking across QM, MD, and ML.
• Deploy simulation tools for internal teams; collaborate with software/product on external deployment.
• Integrate neural network potentials into QM/MD workflows with ML team.

Skills

QM/MD simulations

Python programming

ML integration

reproducible workflows

Education

PhD in computational chemistry/related field

Master's degree with 3+ years relevant experience

Tools

VASP

Gaussian

ORCA

GROMACS

LAMMPS

CP2K

Docker

Job description

Dayhoff Labs seeks a highly skilled computational chemist to push forward AI-accelerated molecular simulations. You will run QM/MD workflows, build reproducible pipelines across QM, MD, and ML, and deploy tools for internal teams while coordinating with software and ML groups.

Ideal candidates have a PhD or Masters with extensive experience, proficiency in Python and major QM/MD packages, and a track record implementing neural network potentials in chemistry workflows.