Staff ML Scientist – Neuromorphic Computing & Spiking Neural Networks

We are hiring a Staff ML Scientist to lead research and development of spiking neural network (SNN) architectures for ultra-low-power, real-time edge inference. This rare role sits at the convergence of computational neuroscience and production machine learning, requiring expertise in neuromorphic hardware platforms (Intel Loihi 2, BrainChip Akida, SynSense), temporal coding schemes, and spike-timing-dependent plasticity (STDP) learning rules.

You will design brain-inspired ML models that achieve orders-of-magnitude improvements in energy efficiency over traditional deep learning for time-series, sensor fusion, and event-driven vision applications.

Candidates must have hands-on experience training and deploying SNNs, deep familiarity with frameworks like Norse, snnTorch, or Lava, and ideally published work in neuromorphic engineering or computational neuroscience. PhD required. Experience bridging the gap between neuromorphic research prototypes and production-ready inference systems is critical.

Key Skills: Spiking Neural Networks, Neuromorphic Computing, Intel Loihi, snnTorch, Norse, Lava, Edge ML, Sensor Fusion, PyTorch, STDP

This is a remote-first position based in North Carolina.