Job Description
Are you ready to architect the next evolution of computing? FutureScale Labs is at the forefront of the 2026 technological revolution, pioneering Neuromorphic Computing—a paradigm shift that mimics the human brain to achieve unprecedented energy efficiency and processing power. We are seeking a visionary Senior Neuromorphic Systems Architect to lead the design of our next-generation silicon cortex.
In this role, you won't just be writing code; you will be designing the nervous system of future AI. You will bridge the gap between neuroscience and hardware engineering, creating architectures that can process information in real-time, biologically-inspired ways. Join us in shaping the intelligence of the year 2026 and beyond.
Why join FutureScale Labs?
- Impactful Work: Directly contribute to the future of human-AI interaction.
- Elite Team: Collaborate with Nobel laureates and industry veterans.
- Equity Package: Competitive stock options in a high-growth unicorn.
Responsibilities
- Architectural Design: Lead the end-to-end design of neuromorphic hardware architectures and their associated software stacks.
- Spiking Neural Networks (SNN): Develop and optimize algorithms that run efficiently on non-von Neumann hardware architectures.
- Prototyping: Oversee the transition from theoretical models to silicon prototypes using FPGA and ASIC technologies.
- Cross-Functional Leadership: Mentor a team of hardware engineers and firmware developers, fostering a culture of innovation and precision.
- Research Integration: Translate cutting-edge neuroscience research into scalable engineering solutions.
- Performance Tuning: Continuously optimize power consumption and latency for edge-computing applications.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Electrical Engineering, Neuroscience, or a related field with a focus on computational neuroscience.
- Experience: 8+ years of experience in systems architecture, with at least 3 years specifically focused on neuromorphic computing, AI hardware, or parallel processing.
- Technical Skills: Proficiency in Python, C++, and Verilog/VHDL. Experience with deep learning frameworks adapted for spiking neural networks (e.g., Brian2, Nengo).
- Domain Knowledge: Deep understanding of brain-inspired computing paradigms, including synaptic plasticity and spike-timing-dependent plasticity (STDP).
- Soft Skills: Exceptional communication skills, with the ability to explain complex technical concepts to diverse stakeholders.