Ml-Driven Energy Materials Innovation Postdoctoral Fellow

Summary

Join SandboxAQ, a high-growth company delivering AI solutions, as a creative and agile researcher in machine learning-driven materials discovery. You will drive innovation by rapidly prototyping and testing hypotheses focused on next-generation energy storage. This role involves developing solutions using hybrid ML and computational chemistry methods, designing automated workflows, and translating ideas into actionable research. Collaboration with cross-functional teams and publication of findings are key aspects. The position is remote, with a salary range of $115k-$135k, and offers potential for bonuses and equity.

Requirements

• Currently enrolled or recently completed a STEM PhD Program in Materials Science, Mechanical Engineering, Physics, Chemistry, Computational Science, or related fields
• Extensive experience applying both (classical or ML-based) molecular dynamics and density functional theory for energy storage applications
• Experience modeling electrochemical energy storage systems and strong understanding of their degradation mechanisms
• Expertise in developing data pipelines for computational chemistry experiments

Responsibilities

• Drive innovation in machine learning-driven energy materials discovery by rapid prototyping and testing of bold hypotheses
• Develop quick-turnaround solutions using hybrid ML and computational chemistry methods, pursuing high-risk, high-reward research focused on energy storage
• Design and refine automated workflows for high-throughput materials screening
• Translate moonshot ideas into actionable research trajectories, effectively communicating insights to diverse audiences, from experts to non-experts
• Remain up to speed with emerging AI techniques, rapidly adapting external advancements to the materials science domain as appropriate
• Collaborate effectively with cross-functional teams of engineers, scientists, and domain experts to innovate, refine, or develop cutting-edge ideas and algorithms
• Conduct large-scale synthetic data generation campaigns using simulation tools (DFT, MD, physics-based models) to study energy storage materials (interfacial stability, ion transport, degradation)
• Publish findings in high-impact journals and present at conferences

Preferred Qualifications

• Preferred candidates have domain knowledge in battery materials, particularly for lithium-ion and other cell chemistries (e.g., cathodes, anodes, electrolytes)
• Fluency with Python and Linux
• Strong research track record, evidenced by high-impact publications or relevant project contributions

Benefits

• Annual discretionary bonuses
• Equity
• Competitive salaries
• Stock options depending on employment type
• Generous learning opportunities
• Medical/dental/vision
• Family planning/fertility
• PTO (summer and winter breaks)
• Financial wellness resources
• 401(k) plans