Senior 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 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 workstreams 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

Learned simulators provide reliable environments for policy learning and control

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

Policies learn faster or generalize better by training inside learned models

Systems successfully bridge simulation and reality through digital twins, online adaptation, or related approaches

Important modeling and scaling risks are identified and addressed early

Research advances translate into measurable platform and product progress

Major research workstreams move from hypothesis to validated system capability

The broader team moves faster and makes stronger technical decisions because of your contributions

TBC develops a clear understanding of when video models create leverage—and when they do not

Preferred Qualifications

PhD or MS in Computer Science, Machine Learning, Robotics, or a related field

Research or industry experience in world 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