Staff AI Researcher

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