At Mythic, we pride ourselves in creating a culture where all employees feel valued and appreciated for the diverse perspectives and backgrounds they bring to the team. We aim to hire smart people, give them the resources they need to do their job well, and then leave the rest up to them. We celebrate individual differences and encourage people to be comfortable bringing their authentic selves to work. At the end of the day, we are committed to building a diverse workforce where everyone belongs.
Mythic is an equal opportunity and affirmative action employer. It ensures equal employment opportunity without discrimination or harassment based on race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, national origin, marital or domestic/civil partnership status, genetic information, citizenship status, veteran status, or any other characteristic protected by law.
We look forward to reviewing your application!
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Sits at the intersection of Analog, AI, Firmware and Silicon Productization,
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Models analog effects and their impact on neural network performance.
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Develops signal-processing based solutions to mitigate impact of analog impairments on neural network accuracy
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Works cross-functionally to validate, debug, and optimize analog compute hardware.
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Contributes to the design of next-generation hardware.
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Brings up new silicon, characterizes silicon performance and develops effective approaches for silicon screening
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Builds frameworks for large-scale data capture and statistical error analysis for analog compute in the simulation domain and on actual silicon hardware
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Own various aspects of algorithms and DSP blocks that optimize the performance of Mythic’s unique analog compute-in-memory technology from concept to customer deployment. This includes calibration loops, non-linearity compensation, offset-correction and estimation of residual-errors.
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Work with model-training, compiler and firmware teams to productize these algorithms.
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Write and modify firmware as needed to productize/debug algorithms
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Continually improve on the fidelity of our modeling and simulation environment to better predict silicon performance.
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Correlate errors seen on silicon to simulation models and contribute to improving the fidelity of our models for analog compute.
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Develop Python frameworks for data collection, error-analysis and quantify impact of analog impairments on neural-network accuracy
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Silicon bring-up, Characterization and Performance-Optimization.
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Bachelor's degree in Electrical Engineering, Computer Engineering, Mathematics, Physics or a related field.
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At least 5 years experience in production DSP or RF baseband engineering (
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Strong familiarity with production Python coding, including object oriented and/or functional programming
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Strong familiarity with core DSP concepts, including frequency domain analysis, filtering, statistical signal processing and estimation theory
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Track record of shipping silicon with DSP or RF/Analog sub-systems.
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Understanding of linear algebra concepts, including matrix math and linear regression.
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Comfort with large-scale collection and processing of signals.
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Commitment to quality and engineering excellence.
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Strong communication skills.
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MS/PhD in Electrical Engineering, Computer Science, Mathematics, Physics or related field.
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Experience with RF calibration and silicon-bringup in the high-speed communication space
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Strong familiarity with NumPy/SciPy (or experience with Numpy and strong familiarity with MATLAB for DSP).
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Familiarity with state-of-the-art neural network architectures