Research Scientist (AI Simulation)

Location: Remote within the United States; only US Persons, including permanent residents or citizens, can be considered.

Salary: $134,400–$201,600, $151,200–$226,800, or $168,000–$252,000 annually, depending on US geographic pay tier.

Company

SandboxAQ develops AI solutions and Large Quantitative Models for challenges in life sciences, financial services, navigation, cybersecurity, and other sectors.

What you will do

• Execute, analyze, and document DFT and multiscale simulations of Fe- and Co-based intermetallic magnet systems.
• Develop computational workflows, machine-learning interatomic potentials, and surrogate models for high-throughput materials screening.
• Generate high-fidelity data for magnetism-aware Large Quantitative Models, including energetics, descriptors, and uncertainty estimates.
• Connect atomistic simulations with microstructure and process-level predictions for powder metallurgy, sintering, and heat treatment.
• Collaborate with experimental partners through Design-Build-Test-Learn cycles to validate and improve model predictions.
• Communicate findings through presentations, publications, patents, and partner-facing technical materials while supporting junior scientists and interns.

Requirements

• 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 and strong Python programming skills.
• Experience applying machine learning, surrogate modeling, or high-throughput methods to materials problems.
• US permanent resident or citizenship status is required because of US Government contractual requirements.
• Ability to collaborate with experimental and cross-functional teams to validate and iterate on predictions.
• Experience with magnetic or intermetallic materials, ML interatomic potentials, HPC, or finite-temperature magnetism is valuable for the role.

Nice to have

• Experience with VASP, Quantum ESPRESSO, LAMMPS, ASE, MACE, NequIP, Allegro, FairChem, PyTorch, or JAX.
• Experience with rare-earth-lean permanent magnets, Fe-N, Mn-based, or Sm-Co systems.
• Knowledge of atomistic spin dynamics, micromagnetic simulation, Bayesian optimization, active learning, or autonomous materials discovery.
• Publications or patents in magnetic materials, computational chemistry, or AI for materials science.
• Experience with CHIPS Act, DOD, DOE, export-control, or federally funded R&D programs.

Culture & Benefits

• Multidisciplinary collaboration across AI, chemistry, physics, mathematics, medicine, engineering, and cybersecurity.
• Competitive compensation with equity and performance-based incentives.
• Health, dental, and vision insurance, 401(k) matching, and parental leave.
• Flexible work arrangements, generous paid time off, and respect for focus time and recovery.
• Mentorship, senior scientific and executive exposure, and dedicated learning budgets.