Drimva

SandboxAQ

Head of Science, Permanent Magnets

$224K–$420K

United States · Remote · Director · Full-time · Posted Oct 20, 2026

Experience
7+ years of post-PhD experience.
Education
PhD in Materials Science, Physics, Chemistry, or a related discipline.

About the job

Leads the scientific strategy, team, and partner engagement for developing manufacturable, high-performance permanent magnets with reduced reliance on rare earth elements.

What you'll do

  • Set the Magnets vertical’s scientific charter and technical roadmap, and guide computational modeling, AI platform capabilities, and experimental validation.
  • Build and lead a multidisciplinary research team; manage IP strategy and represent the program to scientific, industrial, government, and defense partners.
  • Translate partner and application needs into research priorities and support licensing, co-development, and external scientific communications.

What they ask for

  • PhD in Materials Science, Physics, Chemistry, or a related discipline, with deep specialization in magnetism, intermetallics, or hard magnetic materials.
  • 7+ years of post-PhD experience leading scientific programs in magnetic materials, including significant industrial or national-lab leadership at principal scientist, fellow, or director level.
  • Ability to translate first-principles, multi-scale, and ML-driven modeling into manufacturable magnet formulations, including powder metallurgy, sintering, and heat treatment.
  • Track record of leading multidisciplinary teams and research roadmaps, with publications and patents and the ability to represent work to senior government, defense, and industrial audiences.

Nice to have

  • Hands-on experience with Fe-N, Mn-based, Sm-Co, or other rare-earth-lean permanent magnet systems and bulk processing.
  • Experience with federally funded R&D programs, NewCo formation, IP licensing, or scaling magnet research into manufacturing.

Pay

$224K – $420K • Multiple Ranges (base salary)

Tools

Large Quantitative Models (LQMs), AISim, Design-Build-Test-Learn (DBTL)

AI focus

Shape how AI and magnetism-aware Large Quantitative Models are applied to materials discovery and magnet development; prioritize data generation and multi-scale modeling capabilities.

Model-written summary · the original posting is authoritative

First seen Oct 27, 2026 · Last verified Nov 5, 2026 (UTC)