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The Biological Computing Co. (TBC) is an applied biological computing company that uses real neurons to improve AI models. The company studies how biological neural networks process information, extracts computational principles, and translates those insights into software that makes modern AI models better, faster, and more efficient. TBC's 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.
As a Staff AI Researcher, you will help set the technical direction for one of TBC's core research and product areas, specifically focusing on next-generation video generation models that enable robots to learn, plan, and act through imagined futures. This is a hands-on technical leadership role where you will make high-leverage architectural decisions, anticipate modeling and scaling risks, and partner closely with founders and the product team to translate research into deployable systems.
Key responsibilities include: setting technical direction for TBC's generative video modeling platform (modeling, training, evaluation, deployment); designing video generation models supporting expressive latent representations and control-oriented prediction; improving long-horizon rollout fidelity under autoregressive use; integrating video priors and object-centric representations into control systems; anticipating architectural and scaling bottlenecks; establishing technical standards; and multiplying team output through mentorship and collaboration.
You should have a strong background in machine learning, computer vision, robotics, or related fields, with deep experience in generative models (diffusion, autoregressive video, sequence models), model-based reinforcement learning, or physics-informed learning. You need strong technical judgment, a track record of consequential architectural decisions, and the ability to reason about failure modes in long-horizon prediction and control. Experience taking ambiguous research problems from first principles through implementation and evaluation is essential. You should be comfortable working across the stack—modeling, training systems, evaluation, and deployment—and able to partner closely with founders and product leaders to define priorities and convert research into product capability.
Preferred qualifications include a PhD or MS in Computer Science, Robotics, Machine Learning, or related field; research or industry experience in video generation, embodied AI, robot learning, or learned simulation; experience training policies inside learned simulators; action-conditioned video prediction; connecting learned models to real robotic systems; familiarity with latent-action models or cross-embodiment learning; experience with object-centric representations or physical priors; and publications at leading ML, computer vision, or robotics venues.
