About Quantum Formatics

Quantum Formatics is a materials discovery startup building next-generation superconductors for high-field applications, from more accessible MRI technology to scalable fusion energy. Our work is published, patent-pending, and backed by top venture capital investors. Based at The Engine in Cambridge, MA, we are a small, focused team of scientists and engineers operating at the intersection of materials science, AI, and applied physics. We are in a strong financial position with committed institutional backing and are scaling our R&D team to accelerate the pace of discovery.

About the Role

We are seeking a Computational Scientist to join our R&D team and contribute directly to the continued development of our AI-accelerated superconductor discovery algorithm. You will implement methods for direct structure property predction.

Your day-to-day work will span developing and training graph neural network models for materials property prediction, evaluating and integrating state-of-the-art machine learning interatomic potentials, and developing benchmarks to validate model performance against experimental data. You will collaborate closely with our Lead Scientist, as well as with experimental collaborators who synthesize and characterize the materials your models identify. That loop, from prediction to synthesis to characterization and back into model improvement, is the core of what we do.

This is a foundational hire. As the team and the discovery pipeline grow, this role has a clear path toward technical leadership, with the opportunity to shape the direction of our computational platform and mentor and lead future team members.

About you (strongly preferred)

• Ph.D. in Physics, Chemistry, Materials Science, Computer Science, or a closely related field, with a strong focus on atomistic modeling, AI-accelerated materials discovery, or condensed matter physics or relevant research experience.
• At least 3 years of research experience in AI for materials discovery, computational materials science, or a closely related domain.
• Experience with graph neural networks for materials property prediction.
• Familiarity with density functional theory and molecular dynamics simulations.

Pluses

• Familiarity with equivariant architectures (e.g., e3NN).
• Familiarity with Quantum Espresso or similar DFT codes for electron-phonon calculations and phonon property prediction.
• Experience with machine learning interatomic potentials (MLIPs)
• Experience with generative models for crystal structure generation (flow matching, diffusion models, or related architectures).
• Understanding of BCS theory, Eliashberg theory, and the physics of electron-phonon superconductors.
• Experience in Fortran and/or C++.

What We Offer

• Real agency and real impact. At our size, every team member shapes the trajectory of the company. You will work directly with the CEO and CSO on decisions that define our discovery roadmap. The materials you help identify will move from your simulations to synthesis to commercial products that power fusion reactors, MRI systems, and energy infrastructure.
• A genuinely collaborative, mission-driven team. We are scientists who care about impact, not publicity. We work in an environment where ideas are heard, collaboration is the default, and everyone has a stake in the outcome.