Director of Biology
About TBC
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.
Today, 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.
About the Role
TBC is hiring a Director of Biology to lead our San Francisco laboratory and manage the biology team there.
You will be responsible for the scientific quality, experimental throughput and operational performance of the wet lab. You will lead scientists and research staff working across neuronal culture, tissue preparation, multi-electrode array experimentation, electrophysiology, animal research support, laboratory operations and compliance.
This is a senior leadership role for someone who combines strong scientific judgment with effective people management and operational execution. You will remain close to the science while building the people, systems and processes required to make TBC’s biological platform more reliable, reproducible and scalable.
Lead the SF biology team
Manage, mentor and develop scientists, research associates and laboratory staff.
Set team priorities, allocate resources and ensure high-quality execution across experimental programs.
Support recruiting, onboarding, performance management and professional development for the biology organization.
Build a culture centered on scientific rigor, documentation, collaboration, safety, accountability and speed.
Own San Francisco Laboratory Operations
Oversee day-to-day wet-lab operations, including equipment, supplies, inventory, purchasing, vendor relationships and laboratory readiness.
Ensure that facilities, equipment and workflows support current research priorities and future growth.
Lead planning for laboratory expansion, new equipment, maintenance, process improvements and additional experimental capacity.
Identify operational risks and implement systems that allow the laboratory to run safely, consistently and efficiently.
Drive Biological Platform Execution
Oversee neuronal culture systems, tissue preparation and experimental workflows involving living neural networks.
Maintain and improve culture viability, consistency, reproducibility and experimental throughput.
Establish clear quality-control standards for biological preparations and experimental readiness.
Partner with scientists across disciplines to design stimulation, recording and analysis workflows tied to TBC’s biological computing platform.
Oversee Electrophysiology and MEA Workflows
Lead experimental programs involving multi-electrode array systems for neural stimulation and recording.
Ensure the collection of high-quality electrophysiological data with appropriate documentation, quality control and data management.
Develop repeatable experimental protocols that support rapid iteration across biology, computational neuroscience and AI.
Work closely with computational and engineering teams to connect biological measurements to model performance and company milestones.
Maintain Quality, Safety and Compliance
Own laboratory safety, quality and regulatory standards, including GLP-aligned practices, biological safety procedures and standard operating procedures.
Ensure that experimental protocols, training records and laboratory documentation are complete, accurate and current.
Oversee animal research support and compliance with approved IACUC protocols where applicable.
Maintain appropriate practices for equipment use, biological materials, tissue handling, waste disposal and experimental recordkeeping.
Partner Across the Company
Work closely with AI, computational neuroscience, engineering, product and leadership teams to align biology priorities with company objectives.
Translate experimental results, risks and resource requirements into clear recommendations for the technical roadmap.
Help define biological milestones and determine which experimental programs should receive additional investment.
Serve as a senior scientific and operational leader as TBC scales biology from individual research workflows into a repeatable platform.
What Success Looks Like
TBC’s neuronal culture systems are consistent, viable and reproducible across experiments.
Laboratory throughput increases without sacrificing scientific quality, safety or documentation.
MEA experiments generate reliable data that can be translated into measurable AI and model-performance improvements.
Biology priorities, staffing and resources remain aligned with the company’s technical and commercial milestones.
The biology team operates with clear ownership, strong collaboration and high scientific standards.
Laboratory systems and processes scale effectively as TBC expands its team, equipment and experimental programs.
Required Qualifications
B.S., M.S. or Ph.D. in Neuroscience, Cell Biology, Bioengineering or a related field.
At least seven years of relevant wet-lab experience, including two or more years managing scientists, research associates or laboratory teams.
Experience with mammalian cell culture, primary neuronal culture or neural tissue systems.
Strong understanding of laboratory operations, equipment management, biological safety, documentation and quality systems.
Experience managing vendors, inventory, purchasing and complex experimental workflows.
Demonstrated ability to lead interdisciplinary scientific teams and manage multiple priorities.
Strong written and verbal communication skills.
Ability to work full-time on site in TBC’s San Francisco laboratory.
Preferred Qualifications
Ph.D. in Neuroscience, Cell Biology, Bioengineering or a related field.
Experience with multi-electrode array systems, patch clamp or other electrophysiology methods.
Experience with in vivo animal research, including rodent handling, surgical procedures, injections, tissue collection or IACUC compliance.
Familiarity with GLP standards, immunoassays, immunohistochemistry, viral transduction or chemogenetic methods.
Experience in biotechnology, neurotechnology, AI-adjacent research or another interdisciplinary environment.
Experience building or scaling a laboratory through a period of rapid organizational growth.
Experience implementing laboratory quality systems, operational metrics or reproducibility standards.
Staff AI Researcher
About TBC
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.
Today, 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.
About the Role
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.
You will work closely with TBC’s founders, AI researchers, computational neuroscientists, biologists, engineers and product leaders. You will also help translate computational principles discovered through experiments on living neural networks into new video-model architectures, learning approaches and software systems.
What You’ll Work On
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
What We’re Looking For
Strong background in machine learning, computer vision, robotics, or a related field
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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
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
What Success Looks Like
TBC has a clear and scalable technical direction for its video generation-modeling platform
Video generation models remain coherent and useful under their own long-horizon rollouts
Policies learn faster or generalize better by training inside learned simulators
Key architectural and scaling risks are identified and addressed early
Research decisions translate into measurable product and platform progress
The broader team moves faster and makes stronger technical decisions because of your leadership
The team develops a clear understanding of when generative video models help—and when they do not
Preferred Qualifications
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
Experience scaling research systems across large datasets or distributed training environments
Publications at leading machine-learning, computer-vision, or robotics venues
Senior AI Researcher
About TBC
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.
Today, 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.
About the Role
We are building next-generation video generation models that enable robots to learn, plan, and act through imagined futures.
As a Senior AI Researcher, you will own significant research problems within TBC’s video generation-modeling platform. You will design and scale models that serve as reliable foundations for policy learning, control, and real-world deployment.
This is a senior, hands-on research role for someone who can move from first-principles thinking to implementation, experimentation and system-level evaluation. You will make important architectural and modeling decisions, define technical milestones, identify risks early and help determine which research directions should become platform capabilities and products.
You will work closely with TBC’s founders, AI researchers, computational neuroscientists, biologists, engineers and product leaders. You will also help translate computational principles discovered through experiments on living neural networks into new video-model architectures, learning approaches and software systems.
What You’ll Work on
Design video generation models with expressive latent representations, stable rollouts, and control-oriented predictions
Improve long-horizon rollout fidelity under autoregressive use, not only one-step prediction accuracy
Integrate video priors, physical structure, and object-centric representations into learned control systems
Evaluate trade-offs across fidelity, robustness, latency, and inference cost in real robotic settings
Own major research workstreams from hypothesis through implementation, experimentation, and evaluation
Identify modeling, training, and scaling risks before they become blockers
Partner closely with founders, product leaders, engineers, and researchers to translate research into platform capabilities
Support other researchers and engineers through technical guidance, mentorship, and collaboration
What We’re Looking For
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
Hands-on experience designing and training generative models rather than only applying established architectures
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 judgment and experience making meaningful modeling or architectural decisions
Ability to reason clearly about trade-offs across model quality, control utility, latency, robustness, and compute
Comfort working closely with research, engineering, product, and leadership
Evidence of improving the technical quality or effectiveness of the people around you
What Success Looks Like
Learned simulators provide reliable environments for policy learning and control
Video generation models remain coherent and useful under long-horizon rollout
Policies learn faster or generalize better by training inside learned models
Systems successfully bridge simulation and reality through digital twins, online adaptation, or related approaches
Important modeling and scaling risks are identified and addressed early
Research advances translate into measurable platform and product progress
Major research workstreams move from hypothesis to validated system capability
The broader team moves faster and makes stronger technical decisions because of your contributions
TBC develops a clear understanding of when video models create leverage—and when they do not
Preferred Qualifications
PhD or MS in Computer Science, Machine Learning, Robotics, or a related field
Research or industry experience in world 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
Experience scaling research systems across large datasets or distributed training environments
Publications at leading machine-learning, computer-vision, or robotics venues
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