Hi everyone,
I am developing a novel, highly efficient neural backbone architecture designed to drastically reduce inference and training compute costs.
Using a proprietary recurrent, routed-node paradigm that decouples gradient-trained embeddings from non-gradient tracking controllers, the system achieves standard dense-network accuracy on benchmarks while activating only a fraction of the parameter pathway per token step. Recent V4 test runs have successfully verified zero structural regression, perfect state-separation stability, and highly controlled routing convergence.
I am looking for a co-founder or equity-based lead engineer with deep PyTorch fluency (e.g., custom buffer manipulation, advanced tensor routing) to help scale this from prototype to a commercialized enterprise wrapper or SaaS optimizer.
Next milestones include finalizing our V5 temporal gating mechanism and expanding to large-scale task-conditioned routing benchmarks.
If you have strong experience optimizing sparse models, custom attention, or mixture-of-experts (MoE) architectures and want to build something highly proprietary from the ground up, let’s connect. PM me with your background, and we can set up a call under a mutual NDA.