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