Senior AI Researcher
About TBC
The Biological Computing Co. (TBC) is harnessing the brain’s intelligence to evolve how we compute. Our platform integrates living neurons with modern AI systems, creating frontier models that are more stable, scalable and dramatically more efficient.
We are the first to deploy neuron-based alternatives to brute force scaling, with applications across computer vision, generative video, world models and next-generation AI infrastructure.
TBC’s neuroscientists, computational AI engineers and neurobiologists come from institutions such as Apple, John Hopkins, Meta, MIT, and Stanford.
About the Role
We are building next-generation world models that enable robots to learn, plan, and act through imagined futures.
As a Senior Research Scientist, you will design and scale action-conditioned models that serve as reliable foundations for policy learning, control, and real-world deployment. You will work at the intersection of generative modeling, dynamics learning, and robotics, with the goal of making learned simulators useful—not just predictive—for real-world decision-making.
You will take ownership of significant research problems, make important architectural decisions, anticipate modeling and scaling risks, and collaborate closely with founders, product leaders, and other researchers. You will also help raise the technical quality and output of the wider team through strong research judgement, mentorship, and hands-on execution. In this role you will:
Design world models for prediction and control
Design action-conditioned world models with expressive latent representations, stable rollouts, and control-oriented predictions.
Improve long-horizon fidelity under autoregressive use, not only one-step prediction accuracy.
Integrate video priors, physical structure, and object-centric representations into learned control systems.
Explore latent-action interfaces for cross-embodiment transfer, including human-to-robot transfer.
Build closed-loop learning systems
Develop methods for policy learning inside learned simulators, including actor-critic learning over imagined trajectories.
Build closed-loop training pipelines in which models and policies co-evolve.
Develop systems that bridge simulation and reality through digital twins, online adaptation, or related approaches.
Evaluate trade-offs across fidelity, robustness, latency, and inference cost in real robotic settings.
Shape technical decisions
Own major research workstreams from initial hypothesis through implementation and evaluation.
Make high-leverage architectural decisions across model design, training, evaluation, and deployment.
Identify technical and scaling risks before they become blockers.
Partner closely with founders, product leaders, engineers, and researchers to translate research into platform capabilities.
Support and mentor other researchers, improving the quality and pace of technical execution across the team.
Must-Have Requirements
Strong background in machine learning, computer vision, robotics, or a related field.
Hands-on experience designing and training generative models, including diffusion models, autoregressive video models, or related sequence architectures.
Experience with at least one of the following:
World models or learned dynamics models
Generative video modeling
Model-based reinforcement learning or planning
Robot learning or embodied AI
System identification, physics-informed learning, or simulation
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 judgement and experience making consequential modeling or architectural decisions.
Ability to communicate research direction clearly and collaborate effectively across research, engineering, product, and leadership.
Nice-to-Haves
Experience training policies inside learned simulators or 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.
PhD or MS in Computer Science, Machine Learning, Robotics, or a related field.
You’ll Thrive in This Role If You
Enjoy owning difficult research problems without a predefined playbook.
Move comfortably between first-principles research, hands-on implementation, and system-level decisions.
Care about whether models remain useful under closed-loop operation, not only whether they perform well on offline benchmarks.
Can identify promising research directions while recognising approaches that are unlikely to work in real systems.
Are excited to work in an early-stage startup where priorities evolve quickly.
Take ownership, move quickly without heavy supervision, and drive problems through to resolution.
Enjoy helping other researchers and engineers make stronger technical decisions.
What Success Looks Like
World models remain coherent and useful across long-horizon rollouts.
Learned simulators provide reliable environments for policy learning and control.
Policies learn faster or generalise better by training inside these models.
Systems successfully bridge simulation and reality, including digital-twin and online-adaptation settings.
Important architectural and scaling risks are identified and addressed early.
Research advances translate into meaningful platform and product capabilities.
The broader team moves faster and makes stronger technical decisions because of your contributions.
TBC develops a clear understanding of when world models create leverage—and when they do not.