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

As a Staff AI Researcher, you will set the technical direction for TBC’s generative video modeling platform, making high-level architectural decisions, anticipating risks, and translating research into deployable systems. This hands-on leadership role requires solving foundational research problems and elevating the team's output.

Responsibilities

• Set the technical direction for TBC’s generative video modeling platform, including core modeling, training, evaluation, and deployment decisions
• Design video generation models that support expressive latent representations, stable rollouts, and control-oriented prediction
• Improve long-horizon rollout fidelity under autoregressive use, not just one-step accuracy
• Integrate video priors, physical structure, or object-centric representations into control systems
• Anticipate architectural and scaling bottlenecks before they constrain research or deployment
• Establish technical standards, guide key research decisions, and multiply team output through 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
• Strong technical judgment and a track record of making consequential architectural or research decisions
• Ability to reason clearly about failure modes in long-horizon prediction and control
• Experience taking ambiguous research problems from first principles through implementation and evaluation
• Comfortable working across the stack, including modeling, training systems, evaluation, and deployment
• Ability to partner closely with founders, product leaders, and researchers to define priorities and convert research into product capability
• Evidence of improving the effectiveness and technical output of the people around you
• Deep expertise in computer vision and generative modeling
• Hands-on experience with diffusion models, autoregressive video models, or related generative architectures
• Experience designing and scaling novel research systems rather than only applying established approaches

Skills

• Machine learning
• Computer vision
• Robotics
• Generative models
• Diffusion models
• Autoregressive video models
• Sequence models
• Model-based reinforcement learning
• Planning
• System identification
• Physics-informed learning
• Simulation
• Video generation models
• Embodied AI
• Robot learning
• Learned simulation
• Action-conditioned video prediction
• Controllable generative models
• 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 environments

Experience Level

• Staff

Education Level

• PhD or MS in Computer Science, Robotics, Machine Learning, 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.
• 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.