Software Engineer, Machine Learning
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Overview
Job Description
About Nudge
At Nudge, our mission is to develop the best technology for interfacing with the brain to improve people's lives. We're starting with an approach that we believe can help the most people the fastest, and also allow us to learn as much about the brain as possible: developing a non-invasive, ultrasound-based device that can stimulate and image the brain at high resolution and depth. This is a vertically integrated effort building cutting-edge hardware, software, and research capabilities to create products that can benefit millions — and eventually billions — of people.
To succeed, we need to assemble world-class teams across everything we do. We hire people who are exceptional at their craft, believe hard things are worth doing, and execute relentlessly — people who expect the highest levels of both rigor and integrity from each other.
About the role
As a machine learning engineer at Nudge, you will:
Design imaging algorithms that use in-house ultrasound transducers and high-performance compute to image the brain and skull
Build high-resolution acoustic simulations to model the scattering of sound energy in the skull and precisely predict the dose delivered
Develop computer vision systems to track the motion of a brain target in real time and dynamically adapt drive parameters to maintain consistent targeting
Work closely with mechanical engineers, electrical engineers, ultrasound engineers, transducer designers, and neuroscientists
About you
We are looking for engineers of all levels, with a preference for at least 3 years of industry experience. Regardless of your career level, you should have:
Strong first-principles understanding of engineering, physics, and signal processing.
Experience writing production-level code (Python preferred)
Degree in Computer Science or similar engineering discipline
You do not need prior experience with ultrasound or neuroscience
Shipped products that deliver value in the real world; ideally, you will have solved problems involving messy real-world sensors
High integrity
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