Scientist I/II, Characterization and Composition Analysis

Your Impact at LILA

Lila Sciences is seeking a Scientist I/II, Characterization and Composition Analysis to join the Materials Science team within the Autonomous Science Platform. The Autonomous Science Platform combines experimental science, automation, AI, and software to accelerate materials discovery. This role turns elemental measurements into trusted, machine-readable composition data that supports scientific decisions, automated workflows, and model development.

This is a hybrid experimental and software-facing role, centered on hands-on characterization with substantial analyzer development. You will work with X-ray fluorescence (XRF), SEM-based energy-dispersive spectroscopy (SEM-EDS), wavelength-dispersive spectroscopy (WDS), and related analytical instruments. You will develop and validate methods, prepare standards and calibrations, execute measurements, interpret spectra, and troubleshoot issues from instrument, sample-preparation, and data-quality.

You will also design, write, and maintain the analyzer software behind these workflows. Partnering with AI, software, and automation teams, you will build spectral processing, quantification, automated data ingestion, metadata, provenance, and quality-control systems that make characterization outputs reliable, traceable, and useful at scale.

The ideal candidate is credible at both the instrument and the keyboard: strong experimental judgment in elemental characterization, paired with the ability to turn measurement expertise into shared, tested code and production-quality data workflows.

What You'll Be Building

• Own data quality and integrity for elemental and materials characterization workflows.
• Develop XRF, EDS, and WDS methods, calibrations, and QC procedures.
• Prepare standards, run samples, interpret spectra, and troubleshoot measurement issues.
• Lead investigations for anomalous or out-of-specification data and determine data validity.
• Design, write, and maintain analyzer software for spectral processing and quantification in partnership with machine learning team.
• Build automated data ingestion, metadata, provenance, and quality-control workflows.
• Maintain instrument readiness through calibration, documentation, SOPs, and troubleshooting.
• Support safe operation of general materials characterization workflows (profilometry, XRD, SEM).
• Help evaluate, bring online, and develop methods for new analytical detectors as we expand characterization capability.
• Collaborate with hardware, automation, AI, and software teams to scale trusted data generation.

What You'll Need to Succeed

• 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.
• In-depth knowledge of the physical principles behind XRF, EDS, and WDS analysis, including signal generation, matrix effects, calibration, quantification, and common sources of error.
• Strong understanding of calibration, quantification, spectral interpretation, and data-quality control.
• Experience preparing standards, validating methods, and troubleshooting measurement issues.
• Experience writing analysis notebook and maintaining shared, tested code in a version-controlled codebase.
• Proficiency with Python or an equivalent coding environment for data analysis, automation, or analyzer development.
• Ability to translate characterization workflows into reliable, structured, machine-readable data.
• Clear communication skills for working across scientific, AI, automation, and software teams.

Bonus Points For

• Experience with EPMA or related elemental microanalysis workflows.
• Experience with broader characterization techniques such as electron microscopy, optical microscopy, XRD, XPS, ICP-OES, ICP-MS, SIMS, or related analytical methods.
• Familiarity with spectral quantification or analysis tools such as fundamental parameter models, PyMCA, DTSA-II, Probe Software, BadgerFilm, or similar packages.
• Experience developing calibration workflows, reference standards, SOPs, or QC systems for analytical instruments.
• Experience building training datasets or automated analysis pipelines from instrument data.