Computational Neuroscientist

About TBC

The Biological Computing Co. (TBC) is an applied biological computing company that uses real neurons to improve AI models.

We study how biological neural networks process information, extract useful computational principles and translate those insights into software that makes modern AI models better, faster and more efficient. Our Algorithm Discovery Platform brings together biology, computational neuroscience, AI research and software engineering to develop new algorithms, architectures and neurally-optimized software for generative video and next-generation AI infrastructure.

Today, we are commercializing neurally optimized models that run on conventional GPU and cloud infrastructure. Longer term, we are building toward real-time biological compute, where real neurons operate alongside silicon as part of the compute stack.

Our interdisciplinary team includes researchers and engineers with experience at Apple, Johns Hopkins, Meta, MIT, Stanford and other leading institutions.

TBC is seeking a Computational Neuroscientist to help derive novel algorithms and model improvements for AI from understanding the dynamics of real neurons.

You will work across computational neuroscience, biology and machine learning to design experiments, analyze large-scale neural recordings and build models that connect living neural systems with modern foundation models. Your work will sit at the center of TBC’s Algorithm Discovery Platform: identifying where AI models fail, studying how biological neural networks approach related problems and translating what we learn into usable software.

This is a hands‑on, high‑ownership role for someone who wants to help define a new field. You will work closely with wet‑lab biologists, AI researchers and engineers to move from experiment to mathematical principle to model performance.

• Design experiments that encode temporal, spatial and multimodal information into living neural cultures.

• Develop stimulation and information-encoding paradigms for high-density multi-electrode array systems.

• Define experimental controls, baselines and validation criteria that distinguish useful biological effects from noise or generic dynamical behavior.

• Partner with the biology team to improve culture readiness, experimental consistency and reproducibility.

• Analyze large-scale electrophysiological recordings from high-density MEAs and related neural‑interface platforms.

• Model neural population dynamics, latent spaces, neural manifolds, temporal structure, effective connectivity and state transitions.

• Develop methods for decoding neural responses and identifying computationally useful spatial and temporal patterns.

• Characterize how neural networks respond, adapt, learn and retain information across different stimulation conditions and time scales.

• Work with AI researchers to convert neural dynamics into mathematical principles, architectures, adapters, optimizers and learning rules.

• Test whether biologically derived principles improve generative video, world models, inference efficiency, continual learning, memory or generalization.

• Compare biological approaches against strong non-biological controls and surrogate models.

• Determine which properties of the biological response are necessary for model improvement and which can be simplified for scalable software implementation.

• Evaluate discoveries across model sizes, datasets, architectures and modalities.

• Help build tools for neural stimulation, real-time readout, experiment orchestration, data analysis and rapid iteration.

• Develop reusable analysis pipelines and computational tools that connect wet‑lab experiments with AI-model evaluation.

• Support closed-loop systems in which model results inform biological experiments and biological measurements inform the next model iteration.

• Contribute to TBC’s longer-term work in latent-space interfacing, neural cont rollability, connectome-guided learning and real-time biological inference.

• Own research work streams from hypothesis and…