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Scientist, Epitaxial Thin Film Synthesis

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lilasciences logoLilasciences · Cambridge, UK
$108K–$150K/yrFull-timeOn-site2d ago
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Benefits

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.Expected Base Salary Range$108,000 - $150,000 USDAbout LILALila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.We're All InLila Sciences is committed to equal employment opportunity regardlDental insuranceVision insuranceFlexible scheduleEquity / stock optionsPerformance bonusParental leave

Additional Information

Your Impact at LILA As an epitaxial thin film scientist on the Materials Science team at Lila Sciences, you will serve as the lead for synthesis of high-quality epitaxial thin films, contributing to the discovery of novel quantum materials with emergent electronic and magnetic properties. Your domain expertise at the intersection of materials growth, structural characterization, and electronic transport will translate into the method design and automated workflow to enable high throughput experimentation that Lila's autonomous science platform learns from. This work contributes to Lila's materials science programs focused on the predictive design of novel material families and compounds with novel functional properties. You will design growth and characterization protocols that shape how the platform interrogates new material systems, and your experimental outputs feed directly into the next round of campaigns. You will partner with experimentalists, systems engineers, and machine learning scientists, providing the functional-properties knowledge that keeps closed-loop campaigns scientifically grounded and accelerating toward next-generation materials systems. What You'll Be Building Grow single-crystal epitaxial thin films and heterostructures using epitaxial deposition methods (sputtering, pulsed-laser deposition, molecular beam epitaxy). Optimize deposition conditions (substrate temperature, flux ratios, growth rate) to achieve atomically sharp interfaces and target crystal phases. Analyze XRD and AFM data. Design, fabricate, and measure electronic transport devices, including temperature-dependent resistivity, Hall effect, and magnetoresistance. Analyze magneto-transport data to extract physical parameters and identify emergent ground states. Maintain deposition and characterization equipment; troubleshoot hardware and vacuum systems. Collaborate with theorists and computational scientists to inform materials selection and interpret experimental results. What You'll Need to Succeed 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. Bonus Points For 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.


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