Job Description
We are standing on the threshold of the 2026 Neural Leap. Nexus Core Systems is pioneering the convergence of human cognition and artificial intelligence, and we need a visionary Senior Neural Interface Architect to lead our R&D division. You will be responsible for designing the low-latency, high-bandwidth pathways that will define how humans interact with machines in the year 2026 and beyond.
In this pivotal role, you will bridge the gap between neurobiology and advanced computing. You will work in a high-performance environment with top-tier neuroscientists and AI researchers to build the infrastructure for the next generation of Brain-Computer Interfaces (BCI). If you are driven by the challenge of ethical innovation and the prospect of literally rewiring the future, we want to hear from you.
Responsibilities
- Architect and optimize proprietary low-latency neural transmission protocols for next-gen BCI hardware.
- Develop real-time signal processing algorithms to interpret and translate neural signals into actionable digital commands with sub-millisecond precision.
- Collaborate with the neuroscience team to map cortical regions and improve signal-to-noise ratios in complex biological environments.
- Ensure all 2026 compliance standards and safety protocols are integrated into the core architecture of our neural platforms.
- Design SDKs and APIs that empower third-party developers to build applications on our neural network infrastructure.
- Lead technical troubleshooting for high-bandwidth data streams and ensure system stability during critical deployment phases.
Qualifications
- Ph.D. in Computational Neuroscience, Computer Engineering, or a related field with a focus on neural interfaces.
- Minimum of 5 years of experience in Brain-Computer Interface (BCI) development or advanced signal processing.
- Expert proficiency in C++, Rust, and FPGA programming, with a proven track record in low-level hardware optimization.
- Deep understanding of deep learning architectures, particularly those applied to time-series neural data.
- Experience with high-frequency data streaming, distributed systems, and edge computing environments.
- Strong grasp of ethical AI principles and data privacy regulations in the medical and technological sectors.