Research Scientist Intern, NMR Analysis Automation

Meta Reality Labs is seeking a Research Scientist Intern to contribute to cutting-edge materials research in support of the next generation of hardware. Accelerating battery materials discovery requires evaluating large numbers of electrolyte formulations, but analytical characterization remains a manual bottleneck. As an intern, you will build an end-to-end automated NMR analysis pipeline that transforms raw spectrometer data into ML-ready descriptors, reducing expert analysis time from hours to seconds per spectrum while maintaining quantitative accuracy. Your work will directly enable closed-loop, AI-driven electrolyte discovery for next-generation batteries.

Responsibilities:

Design and execute data processing pipelines for automated analysis of NMR spectra of liquid non-aqueous battery electrolytes (1H, 7Li, 13C, 19F, 31P nuclei) with a clear, measurable speedup over manual analysis Implement signal processing workflows including denoising, automated peak identification and assignment, and constrained deconvolution for overlapping spectral regions in multi-component electrolyte mixtures Identify the optimum parameters of the pipeline that balance accuracy and throughput Convert NMR spectra into molecular level compositional and structural descriptors (concentrations, chemical structures) to be used for machine learning models Build spectral quality assessment modules that flag problematic spectra and unreliable fits

Qualifications:

Currently pursuing a PhD in analytical chemistry, physical chemistry, chemical engineering, or a related field Experience with solution-state NMR spectroscopy – theory, data interpretation, and quantitative analysis Proficiency in Python for scientific data processing, including experience with spectral analysis or signal processing Experience building automated data processing pipelines or batch analysis workflow Experience with 2D (COSY) NMR and pulse field gradient NMR experiments Experience designing experiments and analyzing data to validate hypotheses (e.g., DOE, statistical analysis, reproducibility studies) Familiarity with machine learning concepts and feature engineering for predictive models Background in electrochemistry or lithium-ion battery materials Familiarity with Bruker hardware and software, including raw data formats Experience with NMR of battery electrolytes, ionic liquids, or concentrated salt solutions