EE Systems Engineer
About The Position
Precision Neuroscience is building a next-generation brain-computer interface (BCI) to heal and empower millions of people living with neurological conditions. Our first product, Layer 7, is designed to help people with severe paralysis operate digital devices using only their thoughts—opening up new possibilities for daily life. Our team brings together experts in neurosurgery, AI and machine learning, microfabrication, electrical engineering, clinical science, and more. We combine deep technical rigor with a people-first mindset to turn breakthrough research into real-world medical solutions. As a Precision employee, you’ll join one of the fastest-moving and best-capitalized companies in the emerging field of brain-computer interfaces. Since our founding in 2021, we have raised more than $180 million, advanced our technology through validation, and initiated human trials with leading hospitals across the country. Our Values: We build for human impact, measuring progress by the lives our work can change. We do no harm, holding ourselves to the highest standards of safety, integrity, and responsibility. We innovate with urgency, because the stakes are high and our users can’t wait. We bring sharp minds, open ears, pairing expertise with curiosity, humility, and respect. And we lead the way, taking ownership of our work and helping to shape the future of our field.
Requirements
- Strong understanding of analog and digital signal processing (filters, sampling, analog to digital conversion), including statistical signal processing (adaptive filters, estimation and detection theory)
- Solid knowledge of analog signal processing imperfections, such as noise, interference, component variations. Experience modeling of analog imperfections is a plus.
- Knowledge of control systems and feedback fundamentals
- Experience with system modeling and simulation frameworks (Python / MATLAB / Simulink / SystemC / SystemVerilog)
- Experience with embedded signal processing, including fixed-point optimization for FPGA or DSP implementation
- Working knowledge of ML frameworks (PyTorch)
Nice To Haves
- Knowledge of digital communication fundamentals and SERDES is a plus
- Experience with on-device ML is a plus
Responsibilities
- End-to-end sensor signal chain analysis, from sensor elements to ML feature extraction
- Analysis and design of advanced signal processing methods for signal acquisition improvements, such as adaptive filters for interference rejection
- Correlation and modeling of practical device performance vs bench device performance
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
