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.
About the Role
We are building next-generation video generation models that enable robots to learn, plan, and act through imagined futures.
As a Staff AI Researcher you will help set the technical direction for one of TBC’s core research and product areas. You will make high-leverage architectural decisions, anticipate modeling and scaling risks, and partner closely with the founders and product team to translate research into deployable systems. This is a hands-on technical leadership role for someone who can solve foundational research problems while raising the output of the broader team.
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
Set the technical direction for TBC’s generative video modeling platform, including core modeling, training, evaluation, and deployment decisions
Design video generation models that support expressive latent representations, stable rollouts, and control-oriented prediction
Improve long-horizon rollout fidelity under autoregressive use, not just one-step accuracy
Integrate video priors, physical structure, or object-centric representations into control systems
Anticipate architectural and scaling bottlenecks before they constrain research or deployment
Establish technical standards, guide key research decisions, and multiply team output through 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
Strong technical judgment and a track record of making consequential architectural or research decisions
Ability to reason clearly about failure modes in long-horizon prediction and control
Experience taking ambiguous research problems from first principles through implementation and evaluation
Comfortable working across the stack, including modeling, training systems, evaluation, and deployment
Ability to partner closely with founders, product leaders, and researchers to define priorities and convert research into product capability
Evidence of improving the effectiveness and technical output of the people around you
Deep expertise in computer vision and generative modeling
Hands-on experience with diffusion models, autoregressive video models, or related generative architectures
Experience designing and scaling novel research systems rather than only applying established approaches
What Success Looks Like
TBC has a clear and scalable technical direction for its video generation-modeling platform
Video generation models remain coherent and useful under their own long-horizon rollouts
Policies learn faster or generalize better by training inside learned simulators
Key architectural and scaling risks are identified and addressed early
Research decisions translate into measurable product and platform progress
The broader team moves faster and makes stronger technical decisions because of your leadership
The team develops a clear understanding of when generative video models help—and when they do not
Preferred Qualifications
PhD or MS in Computer Science, Robotics, Machine Learning, or a related field
Research or industry experience in video generation models, embodied AI, generative video, robot learning, or learned simulation
Experience training policies inside learned simulators or over imagined trajectories
Experience with action-conditioned video prediction or controllable generative models
Experience connecting learned models to real robotic systems
Familiarity with latent-action models, cross-embodiment learning, or learning from human video
Experience with object-centric representations, physical priors, or structured dynamics models
Experience with digital twins, sim-to-real transfer, online adaptation, or closed-loop data collection
Experience scaling research systems across large datasets or distributed training environments
Publications at leading machine-learning, computer-vision, or robotics venues