Research Engineer, Accelerated Quantum Chemistry

Location: Cambridge, MA or London, UK
Start Date: Immediate
Position Type: Full-time

About the Role

We're seeking a computational scientist with exceptional QM/MD expertise to drive breakthrough discoveries in molecular simulation. This role seeks a candidate who is passionate about research, iterates quickly, and isn't afraid to challenge conventional approaches. You'll build simulation pipelines that seamlessly integrate traditional computational chemistry with pre-trained AI-accelerated models, working in a highly collaborative environment where computational insights directly inform experimental design.

What You'll Do

 

• Design and execute QM/MD simulations using conventional computational chemistry packages combined with modern AI-accelerated models for molecular simulation.

• Build and validate robust, reproducible pipeline development, implementation, and benchmarking protocols that bridge quantum mechanics, molecular dynamics, and modern ML techniques.

• Deploy simulation tools for internal research teams and collaborate intensively with software and product teams for external deployment

• Work hand-in-hand with AI/ML researchers to integrate neural network potentials and machine learning approaches into traditional QM/MD workflows

• Take ownership of challenging problems with incomplete information and deliver actionable computational insights under tight timelines

• Pioneer novel approaches that push the boundaries of hybrid classical-ML simulation workflows

 

What We're Looking For
 

Essential Experience:
 

• PhD in computational chemistry, chemical physics, materials science, or related field, OR Master's degree with 3+ years relevant experience

• Strong hands-on experience with quantum mechanics (QM) and molecular dynamics (MD) simulation packages (e.g., VASP, Gaussian, ORCA, GROMACS, LAMMPS, CP2K)

• Demonstrated track record of building computational pipelines and implementing reproducible workflows

• Proficiency in Python and experience with scientific computing libraries (NumPy, SciPy, computational chemistry libraries)

• Experience with machine learning frameworks (PyTorch, TensorFlow) and their integration into computational chemistry workflows

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Highly Preferred:

 

• Experience with neural network potentials and modern AI models for molecular simulation (e.g., graph neural networks, transformer models)

• Knowledge of high-performance computing environments and workflow management systems

• Experience with containerization (Docker) and deployment pipelines

• Background in benchmarking and statistical validation of computational methods

• Track record of translating computational insights into practical applications

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Essential Qualities:

 

• High Agency: You see problems and solve them without waiting for detailed instructions. You own outcomes.

• Engineering Mindset: You build robust, reproducible computational pipelines and think systematically about scalability and automation.

• Scrappy: You're resourceful, adaptable, and comfortable working with imperfect data or incomplete theoretical frameworks.

• Risk-Taking: You're willing to pursue unconventional approaches and aren't paralyzed by the possibility of failure.

• Collaborative: You thrive in tight feedback loops, actively seek input from diverse expertise, and communicate complex technical concepts clearly to both computational and experimental colleagues.

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Why This Role is Different

This isn't a traditional academic or corporate R&D position. You'll be working at the intersection of cutting-edge ML, quantum chemistry, and practical deployment with the freedom to pursue high-risk, high-reward approaches. Our team moves quickly, fails fast, and iterates based on real-world validation. If you're energized by the prospect of seeing your computational pipelines deployed and tested within weeks rather than months, this role is for you.

Apply

Send your resume or CV, a brief cover letter highlighting your most relevant project experience, and a link to any relevant code repositories (e.g., GitHub) to careers@dayhofflabs.com.