Lila Sciences
Scientist I/II, Characterization and Composition Analysis
$108K–$170K
Cambridge, MA USA +1 · Full-time · Posted Sep 14, 2026
- Location
- Cambridge, MA USA
Alewife, Cambridge, MA - Education
- PhD in Materials Science, Chemistry, Physics, Engineering, or a related field, or M.S. with equivalent hands-on characterization experience.
About the job
Develops elemental characterization methods and analyzer software that turn instrument measurements into reliable, machine-readable data for scientific decisions and automated materials-discovery workflows.
What you'll do
- Develop and validate XRF, EDS, and WDS methods, calibrations, and quality-control procedures; prepare standards, measure samples, and interpret spectra.
- Investigate anomalous or out-of-specification results, assess data validity, and maintain instrument readiness and documentation.
- Build and maintain tested analyzer software and automated workflows for spectral processing, quantification, data ingestion, metadata, provenance, and quality control.
What they ask for
- PhD in Materials Science, Chemistry, Physics, Engineering, or a related field, or M.S. with equivalent hands-on characterization experience.
- Hands-on experience with XRF, SEM-EDS, and WDS for elemental analysis, including their physical principles and common sources of error.
- Experience preparing standards, validating methods, troubleshooting measurements, and applying calibration, quantification, spectral interpretation, and data-quality control.
- Experience writing analysis notebook and maintaining shared, tested code in a version-controlled codebase; proficiency with Python or an equivalent coding environment.
Nice to have
- Experience with EPMA or related elemental microanalysis workflows.
- Experience with broader characterization methods, spectral analysis tools, analytical-instrument calibration/QC, or automated analysis pipelines.
Pay
$108,000 — $170,000 USD (base salary)
Tools
XRF, SEM-EDS, WDS, Python, EPMA, PyMCA, DTSA-II, Probe Software
AI focus
Builds characterization data and software workflows in collaboration with AI and machine-learning teams, supporting model development and automated scientific workflows.
Model-written summary · the original posting is authoritative