About the RoleWe 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
