Staff Computational Scientist, Magnetic Materials

About the role

This role involves leading the scientific direction for the Magnets team, a key part of SandboxAQ's Chemical Simulation group. You will define research strategies, build and guide a scientific team, and apply advanced computational methods and AI to accelerate magnet development. This position focuses on creating new, high-performance permanent magnets without relying on rare earth elements.

Key facts

What you'll do

• Define the scientific and technical roadmap for the Magnets team, focusing on Fe- and Co-based intermetallic systems.
• Manage the creation of intellectual property from generative design outputs.
• Build, lead, and mentor a diverse team of scientists, guiding their technical development.
• Prioritize and specify magnetism-aware Large Quantitative Model (LQM) and multi-scale modeling capabilities.
• Lead scientific collaborations with national labs and industrial partners, translating their needs into scientific problems.
• Design feedback loops between computational modeling and experimental validation to speed up the Design-Build-Test-Learn cycle.
• Collaborate with the commercial team to identify buyers for new IP and structure licensing agreements.
• Author external scientific communications for publications, conferences, and investors.

Requirements

• PhD in Materials Science, Physics, Chemistry, or a related field, with deep expertise in magnetism, intermetallics, or hard magnetic materials.
• Over 5 years of post-PhD experience leading scientific programs in magnetic materials, including industrial or national lab leadership at a principal, fellow, or director level.
• Proven ability to translate first-principles, multi-scale, and ML modeling into manufacturable magnet formulations, including powder metallurgy, sintering, and heat treatment.
• History of building and leading multidisciplinary scientific teams and shaping research roadmaps to meet external commitments.
• Strong external scientific presence, demonstrated by publications, patents, and the ability to present work to senior government and industrial audiences.

Nice to have

• Practical experience with rare-earth-lean permanent magnet systems like Fe-N, Mn-based, or Sm-Co, and their bulk processing challenges.
• Familiarity with applications in lithography stages, vacuum pumps, precision actuators, or defense platforms.
• Experience with federally funded R&D programs (CHIPS Act, DOD, DOE), including export control and IP considerations.
• Prior involvement in NewCo formation, IP licensing, or scaling research IP into commercial manufacturing.
• Track record of taking permanent magnet materials from concept through scale-up, qualification, and transfer to manufacturing.
• Ability to translate system-level application requirements (e.g., force density, thermal operating envelope) into composition, processing, and microstructure targets for simulation platforms.

Skills & tools

• First-principles modeling
• Multi-scale modeling
• Machine learning (ML)
• Powder metallurgy
• Sintering
• Heat treatment processes
• Large Quantitative Models (LQMs)

Practical notes

SandboxAQ offers competitive compensation, comprehensive benefits including health, dental, vision, 401(k) with company match, and parental leave. The company supports a flexible hybrid work environment and provides opportunities for professional growth through mentorship and dedicated learning budgets. This role may involve participation in CHIPS Act-funded programs. SandboxAQ is committed to diversity and provides reasonable accommodations for applicants with disabilities.

About the company

SandboxAQ is an AI and quantum technology company led by CEO Jack D. Hidary. The company operates at the intersection of artificial intelligence and quantum science to address complex computational challenges. Hidary guides the organization with a background that bridges academic research and applied technology development. Hidary has collaborated with MIT on a series of research papers focused on AI and deep learning. This work examines the capacity of deep learning networks to generalize effectively beyond their training data. The research contributes to the broader understanding of model reliability and performance in real-world scenarios. Hidary is also the author of Quantum Computing: An Applied Approach. The textbook is in its second edition and is published by Springer. It serves as a practical resource for professionals and students entering the field of quantum computation. SandboxAQ applies these research foundations to develop enterprise solutions. The company focuses on areas where AI and quantum techniques intersect, including simulation, optimization, and security. The team works to translate theoretical advances into practical tools for global organizations. The mission centers on delivering computational capabilities that address problems beyond the reach of classical methods alone.