Neuromorphic Computing Machine Learning Expert

Responsibilities - Design, implement, and evaluate spiking neural networks (SNNs) and other brain-inspired computing models for real-world tasks - Develop hardware-aware ML approaches optimized for neuromorphic chips and event-driven architectures - Apply surrogate gradient methods, spike timing-dependent plasticity (STDP), and other biologically inspired learning rules - Benchmark neuromorphic models against conventional deep learning baselines across accuracy, latency, and energy efficiency metrics - Document methodologies, experimental results, and technical approaches clearly and reproducibly

Required qualifications - Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, ISSCC, or equivalent) - Master's or PhD in Computer Science, Electrical Engineering, Computational Neuroscience, or a related quantitative field - Demonstrated expertise in neuromorphic computing, spiking neural networks, or brain-inspired AI architectures - Strong problem-solving skills and ability to work independently on technical and research tasks

Preferred qualifications - Hands-on experience with neuromorphic hardware platforms (e.g., Intel Loihi, IBM TrueNorth, BrainScaleS, SpiNNaker, or similar) - Familiarity with SNN simulation and training frameworks (e.g., SpikingJelly, Norse, Brian2, or similar) - Experience bridging computational neuroscience and machine learning (e.g., biologically plausible learning, neural coding schemes) - Background in TA'ing or teaching neuromorphic computing, computational neuroscience, or deep learning courses

Why apply - Flexible Time Commitment – Work on your schedule while tackling meaningful research challenges - Startup Exposure – Work directly with an early-stage Y Combinator-backed company, gaining hands-on experience that sets you apart - Exceptional Pay – Project-based pay ranges from $150–$200/hour - Portfolio Building – Gain experience on frontier neuromorphic computing and brain-inspired ML problems - Professional Growth – Sharpen your skills on varied, challenging spiking neural network and hardware-aware modeling tasks

Employment type: Contract Time commitment: 10 hours/week Location: Remote