Staff AI Researcher

San Franciscofull timeOn Site

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senior

$300k–$345k

H1B: yes

Tbc The Staff AI Researcher sets the technical direction for TBC’s generative video modeling platform and leads architectural decisions for core modeling, training, evaluation, and deployment. This role involves hands-on technical leadership and collaboration with interdisciplinary teams to translate biological neural network insights into AI models.

Responsibilities

We are building next-generation video generation models that enable robots to learn, plan, and act through imagined futures.
As a Staff AI Researcher you will help set the technical direction for one of TBC’s core research and product areas. You will make high-leverage architectural decisions, anticipate modeling and scaling risks, and partner closely with the founders and product team to translate research into deployable systems. This is a hands-on technical leadership role for someone who can solve foundational research problems while raising the output of the broader team.
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

Qualification

Strong background in machine learningGenerative modelsSystem identificationComfortable working across the stackPhD or MS in Computer ScienceFamiliarity with latent-action modelsExperience with digital twins

Required

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
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

Preferred

PhD or MS in Computer Science, Robotics, Machine Learning, or a related field
Research or industry experience in video generation 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