Job Description
Are you ready to define the future of human-machine interaction? Omni-Flow Dynamics is at the forefront of the 2026 technology revolution, pioneering non-invasive neural interfaces that bridge biological thought with digital execution. We are seeking a visionary Senior Neural Interface Engineer to join our elite R&D team in San Francisco.
In this role, you won't just be writing code; you will be architecting the neural pathways of tomorrow. You will work alongside world-class neuroscientists and AI researchers to optimize signal processing, reduce latency in brain-computer interfaces (BCI), and create seamless user experiences that feel like magic.
Why join us?
- Future-Proof Technology: Work on cutting-edge hardware-software integration for next-generation neural links.
- Global Impact: Your work will directly improve the lives of millions through assistive technologies and cognitive enhancement.
- Elite Culture: Collaborate with the brightest minds in Silicon Valley, backed by top-tier funding and resources.
If you are passionate about the convergence of neuroscience, machine learning, and embedded systems, we want to hear from you.
Responsibilities
- Design and implement low-latency signal processing algorithms for non-invasive BCI hardware.
- Optimize firmware for high-fidelity data transmission between neural sensors and cloud processing units.
- Collaborate with interdisciplinary teams to translate neuroscientific data into actionable UI/UX interactions.
- Conduct rigorous testing and validation of neural pathways under various real-world conditions.
- Mentor junior engineers and contribute to the technical roadmap for the 2026 product release cycle.
- Debug complex hardware-software integration issues and drive root cause analysis.
Qualifications
- Master’s or PhD in Computer Science, Computational Neuroscience, Electrical Engineering, or a related field.
- Minimum of 5+ years of experience in embedded systems, signal processing, or AI model deployment.
- Proficiency in Python, C++, and CUDA for high-performance computing.
- Deep understanding of neural networks, particularly Recurrent Neural Networks (RNNs) and Transformers applied to time-series data.
- Experience with hardware prototyping (Arduino, FPGA) and sensor integration (EEG, EMG).
- Strong problem-solving skills and the ability to thrive in a fast-paced, experimental environment.