Neuromorphic Computing Machine Learning Expert

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in neuromorphic computing and brain-inspired AI systems. You will complete tasks at the intersection of ML and neuromorphic hardware — including spiking neural network development, hardware-aware model design, and research tasks applied to energy-efficient, event-driven computing architectures. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge neuromorphic AI research, and a strong addition to your research portfolio.

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

Requirements

• 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