Bagel Labs is an AI research lab and infrastructure company building distributed training systems for diffusion-heavy physical AI. Our work started with the Paris family of image and video models and now extends toward physical-AI workloads where world models, action representations, simulation, and heterogeneous compute become first-order bottlenecks.
We ignore years of experience and pedigree. If you have high agency, strong technical taste, and can create momentum in ambiguous markets, we want to hear from you. Every requirement below is flexible for a candidate with enough judgment and network density.
Role Overview
You will own Bagel Labs' physical-AI go-to-market motion. The job is to turn Bagel's distributed-training advantage into a real market wedge with teams building robotics, autonomy, simulation, embodied AI, industrial AI, world-model, and diffusion-heavy physical-AI model stacks.
The near-term bar is concrete: secure 2-3 serious evaluation commitments from physical-AI teams whose workloads can test whether Bagel's DDM architecture matters in practice.
Key Responsibilities
- Define and prioritize Bagel's physical-AI ICP across robotics, autonomy, simulation, industrial AI, embodied AI, video/world-model, and model-stack teams.
- Build a focused GTM pipeline and move the best accounts from first conversation to written technical evaluation, design partnership, or paid pilot.
- Lead technical-commercial conversations with founders, ML leads, infra owners, research leads, and executives.
- Translate Bagel's DDM architecture into clear buyer language around training economics, heterogeneous compute, specialization, and model-stack constraints.
- Package buyer requirements back into research and engineering priorities for the Paris 3 / physical-AI proof motion.
- Build the early GTM operating system: account maps, qualification criteria, evaluation artifacts, deal notes, and learning loops.
First 90 Days
- Produce a ranked physical-AI ICP map across robotics foundation-model teams, autonomy labs, simulation/world-model builders, industrial robotics groups, and AI-infrastructure buyers serving those teams.
- Open the top conversations directly or through warm routes.
- Convert at least two serious conversations into written technical evaluation commitments, paid pilots, or design-partner scopes.
- Package the strongest buyer requirements into concrete research and systems priorities for Bagel's Paris 3 and distributed-training teams.
Who You Might Be
You know how technical markets actually open. You can find the right physical-AI teams, explain why Bagel matters to them, and earn enough trust to get their real workload constraints on the table.
You might be an ex-founder, first business hire, founding GTM lead, technical BD lead, infrastructure GTM operator, or product-minded commercial lead from robotics, simulation, AI infrastructure, autonomy, developer platforms, GPU/cloud, or deep-tech markets.
Desired Skills
- Experience owning early GTM, business development, technical sales, ecosystem, or design-partner motion in a hard technical market.
- Ability to name credible physical-AI teams Bagel should talk to in the next 30 days, and why they would care.
- Technical credibility with AI infrastructure, robotics, simulation, autonomy, model-platform, generative-model, or GPU/cloud buyers.
- Strong written and verbal communication in technical-commercial settings.
- Bias to action, high agency, and independent judgment.
What We Offer
- Direct access to the CEO and core research team.
- Competitive cash plus meaningful equity.
- A chance to define the first GTM wedge for Bagel Labs' physical-AI infrastructure thesis.
- A front-row seat to one of the hardest infrastructure problems in generative AI.