Senior Research Scientist, Battery Materials Simulation

SandboxAQ

1d•$134,400 - $252,000•Hybrid

About The Position

SandboxAQ is seeking a highly skilled Senior Research Scientist in Battery Materials Simulation to join their growing team. This role focuses on developing computational workflows and AI-driven approaches to accelerate the design of next-generation battery materials, including cathodes, anodes, electrolytes, interfaces, and interphases. The ideal candidate will have deep expertise in applying advanced simulation techniques, including Density Functional Theory (DFT), Molecular Dynamics (MD), and machine learning (ML), to battery materials discovery and optimization. Experience modeling surface chemistry, interfacial degradation mechanisms, and electrochemical reaction pathways is highly desirable. As a senior member of the team, the candidate will provide technical leadership, mentor junior scientists, and drive the execution of strategic research programs in collaboration with internal and external partners. SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges, with its Large Quantitative Models (LQMs) powering advances in various sectors. The company is a global, tech-focused team with experts in diverse scientific and engineering fields, emerging as an independent company in 2022. SandboxAQ fosters an environment that encourages creativity, collaboration, and impact, investing in its people to build a thriving global workforce.

Requirements

Ph.D. in Materials Science, Chemical Engineering, Chemistry, Physics, Computer Science, or a related field.
5+ years of industry experience in computational battery materials research beyond the Ph.D.
Strong theoretical foundation in thermodynamics, kinetics, electrochemistry, and materials science.
Proficiency in DFT and atomistic simulation tools (e.g., VASP, Quantum ESPRESSO, CP2K).
Familiarity with state-of-the-art machine learning force fields and frameworks (e.g. MACE, TensorNet, NequIP, Allegro, or FairChem).
Experience modeling surfaces, interfaces, reaction pathways, and electrochemical systems.
Experience training and evaluating ML models for materials property prediction and materials discovery.
Experience with Bayesian optimization, active learning, and autonomous discovery workflows.
Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX).
Experience working with cloud and high-performance computing environments.
Demonstrated ability to independently drive complex technical projects.
Excellent communication and collaboration skills.

Nice To Haves

Extensive experience modeling battery materials, including cathodes, anodes, liquid electrolytes, solid-state electrolytes, and electrochemical interfaces.
Extensive background modeling rare-event phenomena, charge-transfer kinetics, or degradation at the solid-electrolyte interphase (SEI).
Experience developing and deploying ML force fields for battery materials and reactive systems.
Track record of developing or scaling generative models for materials synthesis, crystal structure generation, or unconstrained composition exploration.
Track record of publications in high-impact peer-reviewed journals and/or patents in battery materials, computational chemistry, or AI for materials science.
Experience leading technical programs and mentoring scientists in an industrial or national laboratory setting.
Experience collaborating with experimental teams to validate computational predictions and accelerate materials development.
Demonstrated success in translating computational discoveries into real-world materials innovation.

Responsibilities

Conduct advanced simulations using DFT, MD, enhanced sampling methods, and ML-based approaches for battery materials and electrochemical systems.
Model surface reactions, interfacial degradation mechanisms, and electrochemical processes, including cathode-electrolyte interfaces (CEI), solid-electrolyte interphases (SEI), solid-state electrolyte interfaces, and reaction pathways under operating conditions.
Develop and deploy computational workflows for high-throughput materials screening, reaction modeling, and materials optimization.
Lead high-fidelity data generation campaigns and develop ML force fields and surrogate models for battery materials and interfaces.
Employ data-driven approaches to analyze large computational and experimental datasets to uncover new insights into materials behavior.
Guide project scoping, execution, and delivery while working closely with cross-functional teams.
Provide technical direction for battery research roadmaps, translate high-level project goals into technical milestones, and mentor junior scientists in best practices for both ML and physics-based modeling.
Collaborate with internal teams, academic collaborators, government partners, and industrial customers to deliver impactful materials innovation.
Effectively communicate research findings through scientific publications, conference presentations, client-facing presentations, and technical reports.