SandboxAQ
Senior Research Scientist, Magnets
$134K–$252K
United States · Remote · Senior · Full-time · Posted Sep 21, 2026
- Experience
- 3+ years of hands-on experience post-PhD or equivalent is highly desired; no minimum years stated as a requirement.
- Education
- PhD or equivalent experience in Solid-state Physics, Quantum Chemistry applied to Materials Science, or a related field.
- Visa sponsorship
- Only U.S. Persons (Permanent Residents or Citizens) can be considered at this time.
About the job
Develops computational methods and machine-learning workflows to discover and optimize manufacturable rare-earth-lean or rare-earth-free permanent magnets, validating predictions with experimental partners.
What you'll do
- Run and analyze DFT and multiscale simulations to predict magnetic-material properties.
- Build high-throughput computational and ML workflows, and generate data for the magnetism-aware LQM.
- Collaborate with experimental teams to validate predictions and communicate findings through publications, patents, and presentations.
What they ask for
- PhD or equivalent experience in solid-state physics, quantum chemistry applied to materials science, or a related field.
- Hands-on experience with DFT or atomistic simulation.
- Strong programming skills in Python and modern scientific-computing practices.
- Experience applying machine learning, surrogate modeling, or high-throughput methods to materials problems; ability to collaborate with experimental or cross-functional teams.
Nice to have
- Specialization in alloys, microstructure engineering, magnetism, intermetallics, or hard/functional magnetic materials.
- 3+ years of hands-on post-PhD or equivalent experience applying DFT and atomistic simulation to magnetic or intermetallic materials; proficiency with VASP, Quantum ESPRESSO, LAMMPS, or ASE is also desired.
Pay
$134.4K – $252K (base salary; currency not stated)
Tools
DFT, Python, VASP, Quantum ESPRESSO, LAMMPS, ASE, MACE, NequIP
AI focus
Uses machine learning, surrogate models, ML interatomic potentials, and a magnetism-aware LQM for high-throughput magnet-material discovery, screening, and optimization.
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