Research Engineer, 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

About us

We're reverse-engineering the origin of life — one of the great unsolved problems in science, and one we think AI finally makes tractable. We believe that understanding this transition, from geochemistry to biochemistry, will let us orchestrate molecular networks and build systems that are more capable, adaptive, efficient, and intelligent.

If we succeed, the applications are vast: from catalysis and green synthesis to ab initio synthetic biology and programmable matter. Understanding and harnessing these processes could let ten billion of us thrive on this planet — and let us dream that diverse life keeps evolving and thriving beyond it.

We're a small, diverse team of AI engineers, computational scientists, and bench scientists. We hold ourselves to the rigor of a research institute, but we ship like an engineering firm. Global team, HQs in Cambridge, MA and London, UK.

The role

You'll build simulation pipelines that fuse conventional computational chemistry with AI-accelerated models, in a setting where the simulation and the experiment are on the same clock. Build it, deploy it, watch it get tested — often in the same month.

What you'll do

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

Essential experience

• PhD in computational chemistry, chemical physics, materials science, or related field — or a Master's with 3+ years relevant experience
• Hands‑on experience with QM and MD packages (e.g., VASP, Gaussian, ORCA, GROMACS, LAMMPS, CP2K)
• Track record of building computational pipelines and reproducible workflows
• Proficiency in Python and scientific computing libraries (NumPy, SciPy, computational chemistry libraries)
• Experience with ML frameworks (PyTorch, TensorFlow) and their integration into computational chemistry workflows

Highly preferred

• Neural network potentials and modern AI models for molecular simulation (e.g., graph neural networks, transformer models)
• HPC environments and workflow management systems
• Containerization (Docker) and deployment pipelines
• Benchmarking and statistical validation of computational methods
• Translating computational insights into practical applications

Logistics

Compensation is highly competitive. We're also able to sponsor visas for the right candidate.