Job Description
Are you ready to define the next era of human intelligence?
Aethelgard Technologies is seeking a visionary Lead AI & Neural Interface Engineer to spearhead our R&D efforts for 2026 and beyond. We are building the synaptic bridges between biological cognition and synthetic computation. In this pivotal role, you will architect the neural pathways that will eventually allow seamless, low-latency communication between the human brain and the cloud.
Why Join Us?
- Work on the bleeding edge of Brain-Computer Interface (BCI) technology.
- Shape the ethical frameworks for Post-AGI integration.
- Competitive compensation and equity in a unicorn startup.
- State-of-the-art research facilities in the heart of the Bay Area.
If you are passionate about the intersection of neuroscience, quantum computing, and AI, we want to hear from you.
Responsibilities
- Architect and deploy high-bandwidth neural network models designed for direct brain-computer interfacing.
- Optimize synaptic latency to ensure near-instantaneous data transmission between biological neurons and digital processors.
- Collaborate with a cross-functional team of neuroscientists, ethicists, and quantum physicists to design safe, human-centric AI integration protocols.
- Develop and validate proprietary algorithms for real-time emotional and cognitive state recognition.
- Lead the technical roadmap for 2026 system upgrades, including neural lace and non-invasive sensor integration.
- Mentor junior engineers and foster a culture of innovation and ethical responsibility.
Qualifications
- Ph.D. or Master’s degree in Computational Neuroscience, Computer Science, Electrical Engineering, or a related field.
- 5+ years of experience in Deep Learning, specifically within recurrent neural networks (RNNs) or transformer architectures.
- Hands-on experience with open-source BCI hardware (e.g., OpenBCI, Neuralink) and signal processing libraries (Python, MATLAB).
- Deep understanding of data privacy laws and AI ethics, particularly regarding cognitive liberty.
- Strong proficiency in Python, C++, and GPU acceleration frameworks (CUDA, TensorRT).
- Experience with large-scale cloud infrastructure (AWS, Google Cloud) to handle high-throughput neural data.