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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.

Responsibilities

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

Requirements

Strong background in machine learning, computer vision, robotics, or a related field
Deep experience with generative models, including diffusion, autoregressive video, or sequence models
Deep experience with model-based reinforcement learning or planning
Deep experience with 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
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

Skills

Machine learning
Computer vision
Robotics
Generative models
Diffusion models
Autoregressive video
Sequence models
Model-based reinforcement learning
Planning
System identification
Physics-informed learning
Simulation
Generative model design
Generative model training
Long-horizon prediction
Autoregressive rollout
Model architecture
Training systems
Experimentation
Evaluation
Technical judgment
Modeling decisions
Architectural decisions
Model quality
Control utility
Latency
Robustness
Compute
World models
Embodied AI
Generative video
Robot learning
Learned simulation
Policy training
Learned simulators
Imagined trajectories
Action-conditioned video prediction
Controllable generative models
Real robotic systems integration
Latent-action models
Cross-embodiment learning
Learning from human video
Object-centric representations
Physical priors
Structured dynamics models
Digital twins
Sim-to-real transfer
Online adaptation
Closed-loop data collection
Distributed training
Large datasets
Machine learning publications
Computer vision publications
Robotics publications

Experience Level

Senior

Education Level

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

About the Company

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.