Scientist, Epitaxial Thin Film Synthesis

Lila Sciences Lila Sciences · AI Frontier · Alewife, Cambridge, MA · Autonomous Science Platform

The Scientist, Epitaxial Thin Film Synthesis will lead the synthesis of high-quality epitaxial thin films for novel quantum materials. This role involves designing growth and characterization protocols for an autonomous science platform, partnering with ML scientists, and contributing to predictive material design. The scientist will grow and analyze thin films, design and measure electronic transport devices, and maintain equipment.

What you'd actually do

  1. Grow single-crystal epitaxial thin films and heterostructures using epitaxial deposition methods (sputtering, pulsed-laser deposition, molecular beam epitaxy).
  2. Optimize deposition conditions (substrate temperature, flux ratios, growth rate) to achieve atomically sharp interfaces and target crystal phases.
  3. Analyze XRD and AFM data.
  4. Design, fabricate, and measure electronic transport devices, including temperature-dependent resistivity, Hall effect, and magnetoresistance.
  5. Analyze magneto-transport data to extract physical parameters and identify emergent ground states.

Skills

Required

  • Ph.D. in Physics, Materials Science, Applied Physics, or a closely related field
  • Minimum of 4 years of hands-on experience growing epitaxial thin films (including graduate research)
  • Demonstrated expertise in XRD-based structural analysis of crystalline thin films
  • Proficiency with surface characterization
  • Strong background in electronic and magneto-transport measurements at cryogenic temperatures
  • Experience operating RHEED for in-situ growth monitoring
  • Knowledge of vacuum science and ultra-high-vacuum system maintenance

Nice to have

  • Track record of synthesizing thin film materials exhibiting correlated-electron phenomena (e.g., magnetism, spin-orbit coupling effects, or low-carrier-density transport anomalies)
  • Publication record in peer-reviewed journals commensurate with experience
  • Experience with combinatorial or high-throughput approaches to materials discovery
  • Experience with sputtering processes including DC, RF, and HiPIMS modes
  • Familiarity with lithographic patterning for transport device fabrication
  • Background in Bayesian optimization or machine-learning-guided experimental design

What the JD emphasized

  • Minimum of 4 years of hands-on experience growing epitaxial thin films (including graduate research)
  • Demonstrated expertise in XRD-based structural analysis of crystalline thin films
  • Strong background in electronic and magneto-transport measurements at cryogenic temperatures

Other signals

  • autonomous science platform
  • high throughput experimentation
  • closed-loop campaigns