Job Description:

• Conduct advanced simulations using DFT and MD for solid-state materials, with a focus on predicting properties for solid-state electrolytes and interfacial degradation reactions.
• Employ data-driven approaches to analyze large datasets derived from computational simulations and experiments to uncover new insights into materials behavior.
• Conduct high-fidelity data generation campaigns and develop ML force fields for solid-state materials.
• Guide and scope projects with clear deliverables alongside agile teams.
• Collaborate closely with multi-disciplinary teams to independently prototype and scale cutting-edge, impactful materials design solutions.
• Generate and evaluate hypotheses to assist design decisions and influence project direction by developing and deploying computational methods and workflows.
• Effectively present and communicate research findings through scientific talks, blog posts, client-oriented presentations, and peer-reviewed publications.

Requirements:

• Ph.D. in Materials Science, Chemical Engineering, Chemistry, Computer Science, or a related field is preferred.
• 3+ years of hands-on experience in modeling complex solid-state battery materials, such as cathodes, anodes, solid-state electrolytes, and/or interfacial reactions at non-equilibrium states is highly desirable.
• Proficiency in common DFT and MD simulation software (e.g., VASP, Quantum ESPRESSO, LAMMPS, ASE).
• Experience with developing or using AI models for chemistry and material discovery using popular deep learning frameworks on CPUs and GPUs.
• Proven ability to benchmark and compare domain specific AI models for materials discovery.

Benefits:

• Comprehensive health, dental, and vision insurance;
• 401(k) with company match;
• Generous parental leave;
• Flexible hybrid work arrangements;
• Generous PTO;
• Culture that respects focus time and recovery;
• Direct exposure to CHIPS Act-funded programs;
• Mentorship;
• Dedicated learning budgets to support continued growth.