About the job
FL119 is seeking a creative and deeply technical Neuromorphic Systems Architect to join our team. Leveling is flexible based on experience. This role will focus on translating nonlinear neural dynamics into next-generation computational architectures. You will work at the interface of neuroscience, dynamical systems theory, and hardware engineering to design and prototype electronic architectures that embody biologically derived principles of computation.
Responsibilities
Formalize brain-derived computation using dynamical systems theory and translate these insights into hardware-relevant abstractions.
Architect novel neuromorphic or neuro-inspired hardware systems (analog, mixed-signal, in-memory, or alternative substrates).
Define system-level architecture, including memory models, communication schemes, and energy-efficient compute primitives.
Collaborate with neuroscience, signal processing, and machine learning teams to ensure tight coupling between biological insight and architectural design.
Lead feasibility studies, simulations, and early-stage prototyping efforts.
Evaluate trade-offs across CMOS, emerging devices, photonic, memristive, or other unconventional compute substrates.
Contribute to IP strategy, including invention disclosures and patents.
Help shape the long-term compute vision of the company.
Qualifications
Minimum
PhD (or equivalent experience) in Electrical Engineering, Applied Physics, Computational Neuroscience, Computer Engineering, or a closely related field.
Deep expertise in nonlinear dynamical systems and their application to computation.
Experience designing hardware architectures (ASIC, FPGA, analog/mixed-signal, or emerging device technologies such as RRAM, PCM, memristors, etc.).
Strong mathematical foundation in dynamical systems, control theory, or computational modeling.
Demonstrated ability to move from theory to implementable system design.
5+ years of relevant research or industry experience (level dependent)
Preferred
Prior work in neuromorphic computing or spiking neural network hardware.
Experience with event-driven or asynchronous architectures.
Familiarity with reservoir computing, recurrent dynamical systems, or physical computing substrates.
Experience working in early-stage, fast-paced R&D environments.
Track record of patents or high-impact publications in neuromorphic systems or unconventional computing.