Staff Research Engineer, Neural Architectures

About Ephapsys

We leverage ephaptic coupling to pioneer a new paradigm for empowering and governing autonomous AI agents across industries.

As billions of agents spread across clouds, devices, and biological substrates, Ephapsys aims to be the decentralized identity and integrity layer that keeps every agent trusted, safe, and under control.

We are seeking a Staff Research Engineer, Neural Architectures to lead the advancement of ephaptic coupling technology across modern AI systems.

This role is research-intensive and focused on generating novel, experimentally validated improvements in neural architecture design.

You will work directly with the founder to formalize theoretical models, design controlled experiments, and deliver measurable breakthroughs in performance, computational efficiency, robustness, and adaptability.

The role includes producing publishable research while ensuring practical integration into the Ephapsys SDK and platform.

• Neural Architecture Innovation
• Design and formalize new ephaptic interaction operators within deep learning architectures
• Develop mathematical models describing cross-neuron interaction dynamics
• Integrate ephaptic mechanisms into transformers, MLPs, and hybrid architectures
• Conduct rigorous ablation studies and theoretical analysis of convergence behavior
• Investigate stability properties and dynamical systems implications of modulation layers

• Deliver measurable improvements in training efficiency, inference latency, or generalization
• Explore adaptive modulation strategies for improved robustness and transfer learning
• Analyze computational complexity and scalability trade-offs
• Evaluate hardware-aware and distributed training optimizations

• Produce internal technical reports documenting experimental findings
• Contribute to peer-reviewed publications and conference submissions
• Present research findings in technical forums when appropriate
• Stay current with frontier model research including transformer alternatives and emerging paradigms

• Translate validated research into modular, production-ready SDK components
• Build reusable frontier model modules and research-grade agents
• Establish benchmarking standards to quantify ephaptic performance gains
• Collaborate with security engineering to evaluate new attack surfaces introduced by novel operators

Required

• PhD in Machine Learning, AI, Applied Mathematics, Computational Neuroscience, or related field
• Strong mathematical foundation (linear algebra, optimization, dynamical systems)
• Deep expertise in neural network theory and architecture design
• Advanced proficiency in PyTorch or Tensor Flow
• Experience conducting reproducible research with controlled experimental design
• Demonstrated ability to translate theory into working implementations

Preferred

• Experience training or fine-tuning large-scale models
• Familiarity with distributed training and performance optimization
• Prior publications in reputable AI/ML conferences or journals
• Interest in expanding neural computation beyond synaptic-only paradigms
• Experience contributing to open-source ML frameworks

Within the first 3-6 months, you will have demonstrated experimentally validated performance or efficiency gains through ephaptic integration, contributed to at least one publishable research milestone, and delivered production-ready frontier model capabilities integrated into the Ephapsys platform.

• Shape the future of neural architecture research
• Operate at the frontier of adaptive and secure AI systems
• High research autonomy and technical ownership
• Early-stage equity participation
• Direct collaboration on foundational AI innovation