Extropic
Experimental Physicist
$150K–$250K
Boston · On-site · Full-time · Posted Apr 17, 2026
- Education
- Preferred: PhD in Physics, Applied Physics, Electrical Engineering, or related field with focus on experimental work.
- Visa sponsorship
- Any employment offer is conditioned on authorization to receive controlled items subject to U.S. export control laws and regulations, including the EAR.
About the job
Characterize and validate Extropic’s novel computing architectures through microelectronics experiments, signal processing, and analysis of experimental data.
What you'll do
- Design and execute experiments to characterize microelectronic devices and circuits, including electrical, noise, high-frequency, and temperature-dependent measurements.
- Develop measurement setups and data-acquisition automation, then analyze results and compare them with theoretical models and simulations.
- Collaborate with circuit designers to iterate on device and architecture designs.
What they ask for
- Experience designing and executing experiments to characterize microelectronic devices and circuits.
- Proficiency in experimental signal processing, data analysis, and scientific computing.
- Hands-on experience with laboratory instrumentation and temperature-controlled measurements; understanding of device physics across temperature regimes.
- Experience developing custom measurement setups and data-acquisition automation; analyzing nonlinear dynamics, stochastic processes, or analog computing primitives; and correlating experiments with models or simulations.
Nice to have
- PhD in Physics, Applied Physics, Electrical Engineering, or a related field focused on experimental work.
- Experience with neuromorphic, analog, or unconventional computing; semiconductor or mesoscopic physics; machine learning for experimental data; or CMOS characterization.
Pay
$150K – $250K USD base salary
Tools
oscilloscopes, spectrum analyzers, network analyzers, signal generators, source-measure units, probe stations, FFT
AI focus
Familiarity with machine learning techniques for experimental data analysis and pattern recognition is preferred.
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