Impress employers and recruiters.
Choose from hundreds of resume examples.

Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

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 foundations for policy learning, control, and real-world deployment, working at the intersection of generative modeling, dynamics learning, and robotics.

Responsibilities

• Design action-conditioned world models with expressive latent representations, stable rollouts, and control-oriented predictions.
• Improve long-horizon fidelity under autoregressive use.
• Integrate video priors, physical structure, and object-centric representations into learned control systems.
• Explore latent-action interfaces for cross-embodiment transfer.
• Develop methods for policy learning inside learned simulators, including actor-critic learning over imagined trajectories.
• Build closed-loop training pipelines where 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.
• 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 with founders, product leaders, engineers, and researchers to translate research into platform capabilities.
• Support and mentor other researchers to improve technical execution.

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 world models or learned dynamics models.
• Experience with generative video modeling.
• Experience with model-based reinforcement learning or planning.
• Experience with robot learning or embodied AI.
• Experience with system identification, physics-informed learning, or simulation.
• Strong understanding of long-horizon prediction, autoregressive rollout, and failure modes in model 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 teams.

Skills

• Machine Learning
• Computer Vision
• Robotics
• Generative Modeling
• Diffusion Models
• Autoregressive Video Models
• Model-based Reinforcement Learning
• Embodied AI
• Physics-informed Learning
• Simulation
• Digital Twins
• Distributed Training

Experience Level

• Senior

Education Level

• PhD or MS in Computer Science, Machine Learning, Robotics, or a related field (preferred)

About the Company

• The Biological Computing Co. (TBC) is harnessing the brain’s intelligence to evolve how we compute.
• The platform integrates living neurons with modern AI systems to create stable, scalable, and efficient frontier models.
• TBC is the first to deploy neuron-based alternatives to brute force scaling for applications in computer vision, generative video, world models, and AI infrastructure.
• Team members come from institutions such as Apple, John Hopkins, Meta, MIT, and Stanford.