About SandboxAQ

SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.

We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.

At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.

The Opportunity

Introduction to the team: The Magnets team is one of several research verticals within SandboxAQ's broader Chemical Simulation group, which uses advanced computational methods, physics-based simulation, and AI to discover and develop novel materials. Within that effort, the Magnets vertical is focused on one of the most strategically important materials problems today: developing high-performance permanent magnets that reduce or eliminate reliance on neodymium and other rare earth elements and can be manufactured in the United States. Our goal is to create novel magnet formulations that use existing industry processes and equipment while matching or exceeding the performance of today's leading NdFeB magnets in demanding applications such as semiconductor equipment, vacuum systems, precision motion devices, and defense technologies.

Introduction to the role: We are seeking a Research Scientist to help drive the computational discovery and optimization of next-generation magnet materials. The ideal candidate has a strong background in applying first-principles and multi-scale reference systems, computational simulation, together with machine learning, to magnetic and intermetallic materials. In this role you will (1) run and interpret atomistic and multi-scale simulations that predict the properties of magnets materials, (2) build and deploy computational and ML-driven workflows that screen rare-earth-lean and rare-earth-free candidates at scale, (3) work with experimental collaborators to test and refine predictions against real-world data, and (4) contribute to the project plans, publications, and IP of the vertical. You will report to the Chief Scientist, Magnets and work closely with our AI simulation platform and data-generation teams.

Key Responsibilities

• Execute, analyze, and document DFT and multi-scale reference systems to predict phase stability, magnetocrystalline anisotropy, saturation magnetization, Curie temperature, and other properties across Fe- and Co-based intermetallic systems using U.S.-accessible alloying elements.

• Develop and test computational workflows and ML interatomic potentials / surrogate models that enable high-throughput screening and optimization of rare-earth-lean and rare-earth-free permanent magnet candidates.

• Lead high-fidelity data generation campaigns that feed the magnetism-aware LQM; work with platform and data-generation teams to specify the energetics, descriptors, and uncertainty estimates the workflows require.

• Connect atomistic simulation outputs to microstructure- and process-level predictions (powder metallurgy, sintering, heat treatment) to keep candidate formulations manufacturable with existing industry equipment.

• Collaborate with experimental validation partners to test and iterate on model predictions, contributing to the Design-Build-Test-Learn (DBTL) feedback loop between computation and the lab, ensuring that LQM models are more accurate over time.

• Work within defined projects, deliverables and milestones alongside agile, multidisciplinary teams, and generate and evaluate hypotheses that influence project direction.

• Communicate research findings through scientific talks, peer-reviewed publications, patents, and partner-facing technical presentations; support junior scientists and interns as the team grows.

Essential Skills & Experience

• 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.

• Demonstrated ability to collaborate with experimental or cross-functional teams to validate and iterate on predictions.

• Because this position supports specific U.S. Government contractual requirements, we can only consider US Persons (Permanent Residents or Citizens) at this time.

Highly Desired Skills & Experience

• Specialization in alloys, microstructure engineering, magnetism, intermetallics, or hard/functional magnetic materials.

• 3+ years of hands-on experience (post-PhD or equivalent) applying DFT and atomistic simulation specifically to magnetic, intermetallic, crystal lattices, energy bands, or phonons for material science.

• Proficiency with common DFT and atomistic simulation software (e.g., VASP, Quantum ESPRESSO, LAMMPS, ASE).

• Experience developing or using ML interatomic potentials and AI models for materials discovery (e.g., MACE, NequIP, Allegro, or FairChem) with modern deep learning frameworks (e.g., PyTorch, JAX), and 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 their 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 to application requirements such as force density, thermal operating envelope, and demagnetization margin.

• Familiarity with Bayesian optimization, active learning, or autonomous discovery workflows applied to 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 awareness of export-control and IP considerations.

Why Join Us?

We offer competitive compensation, a comprehensive benefits package, and opportunities for professional growth.

• Compensation: Competitive base salary commensurate with experience, plus equity and performance-based incentives.

• Benefits: Comprehensive health, dental, and vision insurance; 401(k) with company match; generous parental leave.

• Work-Life Balance: Flexible hybrid work arrangements, generous PTO, and a culture that respects focus time and recovery.

• Career Development: Direct exposure to CHIPS Act-funded programs, senior scientific and executive leadership, mentorship, and dedicated learning budgets to support continued growth.

SandboxAQ Welcomes All

We are committed to fostering a culture of belonging and respect, where diverse perspectives are actively sought and valued. Our multidisciplinary environment provides ample opportunity for continuous growth - working alongside humble, empowered, and ambitious colleagues ready to tackle epic challenges.

Equal Employment Opportunity: All qualified applicants will receive consideration regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.

Accommodations: We provide reasonable accommodations for individuals with disabilities in job application procedures for open roles. If you need such an accommodation, please let a member of our Recruiting team know.

Read: Guidance for candidates on using AI Tools in interviews