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SandboxAQ is a high-growth AI company emerging from Alphabet Inc. (2022) that develops Large Quantitative Models (LQMs) to address challenges in life sciences, financial services, navigation, cybersecurity, and materials science. The company is backed by leading growth capital investors and operates as a global, multidisciplinary team.
The Magnets team is a research vertical within SandboxAQ's Chemical Simulation group, focused on computational discovery and optimization of next-generation permanent magnet materials. The strategic goal is to develop high-performance permanent magnets that reduce or eliminate reliance on rare earth elements (particularly neodymium) while being manufacturable in the United States using existing industry processes. Target applications include semiconductor equipment, vacuum systems, precision motion devices, and defense technologies.
In this Senior Research Scientist role, you will drive computational discovery of rare-earth-lean and rare-earth-free magnet candidates. Key responsibilities include: (1) executing and interpreting density functional theory (DFT) and multi-scale atomistic simulations to predict magnetic properties (phase stability, magnetocrystalline anisotropy, saturation magnetization, Curie temperature) across Fe- and Co-based intermetallic systems; (2) developing and testing computational workflows and machine learning interatomic potentials that enable high-throughput screening and optimization; (3) leading high-fidelity data generation campaigns that feed the magnetism-aware LQM, working with platform and data teams to specify energetics, descriptors, and uncertainty estimates; (4) connecting atomistic simulation outputs to microstructure and process-level predictions (powder metallurgy, sintering, heat treatment) to ensure manufacturability; (5) collaborating with experimental validation partners to test and iterate on predictions, supporting the Design-Build-Test-Learn feedback loop; (6) working within defined projects and milestones alongside multidisciplinary agile teams; and (7) communicating research findings through scientific talks, peer-reviewed publications, patents, and technical presentations, while supporting junior scientists and interns as the team grows.
You will report to the Chief Scientist, Magnets and work closely with AI simulation platform and data-generation teams.
REQUIREMENTS:
- PhD or equivalent experience in Solid-State Physics, Quantum Chemistry applied to Materials Science, or 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
- Demonstrated ability to collaborate with experimental or cross-functional teams to validate and iterate on predictions
- US Person status (Permanent Resident or Citizen) required due to US Government contractual requirements
HIGHLY DESIRED:
- Specialization in alloys, microstructure engineering, magnetism, intermetallics, or hard/functional magnetic materials
- 3+ years post-PhD hands-on experience applying DFT and atomistic simulation to magnetic, intermetallic, crystal lattice, energy band, or phonon systems for materials science
- Proficiency with DFT and atomistic simulation software (VASP, Quantum ESPRESSO, LAMMPS, ASE)
- Experience developing or using ML interatomic potentials and AI models for materials discovery (MACE, NequIP, Allegro, FairChem) with modern deep learning frameworks (PyTorch, JAX); familiarity with HPC and cloud environments
- Hands-on experience with Fe-N, Mn-based, Sm-Co, or other rare-earth-lean permanent magnet systems and bulk processing challenges
- Experience with finite-temperature magnetism and coercivity modeling, including atomistic spin dynamics and/or micromagnetic simulation
- Familiarity linking composition and microstructure to processing (powder metallurgy, sintering, heat treatment) and application requirements (force density, thermal operating envelope, demagnetization margin)
- Familiarity with Bayesian optimization, active learning, or autonomous discovery workflows for materials
- Authorship of publications in high-impact peer-reviewed journals and/or patents in magnetic materials, computational chemistry, or AI for materials science
- Experience operating within CHIPS Act, DOD, DOE, or other federally funded R&D programs, including export-control and IP awareness
