Software Engineer, Machine Learning

About NudgeAt 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.We’ve brought together a team of the best, who believe hard things are worth doing. To succeed, we need to assemble world-class teams across everything we do. We hire people who are exceptional at their craft, do the real work, and execute relentlessly — people who expect the highest levels of both rigor and integrity from each other.About The RoleAs 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 skullBuild high-resolution acoustic simulations to model the scattering of sound energy in the skull and precisely predict the dose deliveredDevelop computer vision systems to track the motion of a brain target in real time and dynamically adapt drive parameters to maintain consistent targetingWork closely with mechanical engineers, electrical engineers, ultrasound engineers, transducer designers, and neuroscientistsAbout YouWe 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 disciplineYou do not need prior experience with ultrasound or neuroscienceShipped products that deliver value in the real world; ideally, you will have solved problems involving messy real-world sensors