Impress employers and recruiters.
Choose from hundreds of resume examples.
Impress employers and recruiters.
Choose from hundreds of resume examples.
Tailor your resume to this Staff AI Researcher role.
Rezi rewrites your resume against The Biological Computing Co.'s job description. Free.
Tailor your resume to this Staff AI Researcher role.
Rezi rewrites your resume against The Biological Computing Co.'s job description. Free.
Don't guess if your resume is good enough.
See how it scores against the Staff AI Researcher posting at The Biological Computing Co. — free, in seconds.
Don't guess if your resume is good enough.
See how it scores against the Staff AI Researcher posting at The Biological Computing Co. — free, in seconds.
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.