Senior AI Researcher

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.

About the Role

We are building next-generation video generation models that enable robots to learn, plan, and act through imagined futures.

As a Senior AI Researcher, you will own significant research problems within TBC’s video generation-modeling platform. You will design and scale models that serve as reliable foundations for policy learning, control, and real-world deployment.

This is a senior, hands-on research role for someone who can move from first-principles thinking to implementation, experimentation and system-level evaluation. You will make important architectural and modeling decisions, define technical milestones, identify risks early and help determine which research directions should become platform capabilities and products.

You will work closely with TBC’s founders, AI researchers, computational neuroscientists, biologists, engineers and product leaders. You will also help translate computational principles discovered through experiments on living neural networks into new video-model architectures, learning approaches and software systems.

What You’ll Work On

• Design video generation models with expressive latent representations, stable rollouts, and control-oriented predictions
• Improve long-horizon rollout fidelity under autoregressive use, not only one-step prediction accuracy
• Integrate video priors, physical structure, and object-centric representations into learned control systems
• Evaluate trade-offs across fidelity, robustness, latency, and inference cost in real robotic settings
• Own major research work streams from hypothesis through implementation, experimentation, and evaluation
• Identify modeling, training, and scaling risks before they become blockers
• Partner closely with founders, product leaders, engineers, and researchers to translate research into platform capabilities
• Support other researchers and engineers through technical guidance, mentorship, and collaboration

What We’re Looking For

• Strong background in machine learning, computer vision, robotics, or a related field
• Deep experience with one or more of the following:
• Generative models, including diffusion, autoregressive video, or sequence models
• Model-based reinforcement learning or planning
• System identification, physics-informed learning, or simulation
• Hands-on experience designing and training generative models rather than only applying established architectures
• Strong understanding of long-horizon prediction, autoregressive rollout, and the failure modes that emerge when models operate on their own outputs
• Experience working across model architecture, training systems, experimentation, and evaluation
• Ability to take ambiguous research problems from first principles through implementation
• Strong technical judgment and experience making meaningful modeling or architectural decisions
• Ability to reason clearly about trade-offs across model quality, control utility, latency, robustness, and compute
• Comfort working closely with research, engineering, product, and leadership
• Evidence of improving the technical quality or effectiveness of the people around you

What Success Looks Like

• Video generation models remain coherent and useful under long-horizon rollout
• Learned…