Computational Neuroscientist

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.

• Dimensionality reduction

• Latent-variable models

• Neural manifolds

• Dynamical-systems modeling

• Encoding and decoding models

• Time-series analysis

• Effective-connectivity analysis

• Statistical modeling and uncertainty analysis

• Ability to design rigorous experiments and distinguish correlation from causal or mechanistic evidence.

• Ability to communicate clearly and work effectively with wet-lab scientists, AI researchers and engineers.

• Strong scientific judgment, ownership and comfort operating in a fast-moving research environment where the playbook is still being written.

• Experience with closed-loop neural interfaces, adaptive stimulation or real-time neural decoding.

• Experience with causal inference, connectomics, synaptic plasticity, STDP or effective-connectivity estimation.

• Familiarity with foundation models, generative video, world models, reinforcement learning or model-representation analysis.

• Experience with reservoir computing, neuromorphic computing, biological computing or other nontraditional compute substrates.

• Experience connecting population-level neural dynamics to machine-learning architectures.

• Familiarity with PyTorch, JAX or other modern deep-learning frameworks.

• Experience building reusable research infrastructure, analysis pipelines or internal scientific tools.

• Publications at leading neuroscience, neural-engineering or machine-learning venues.

• Interest in translating frontier research into products that improve real AI systems.