Backed by Initial Capital.
About the role
We are seeking a visionary AI/ML Research Intern to join our founding team and bridge the gap between high-level AI research and real-world physical actuation for our next-generation autonomous transport platforms. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Education: Currently enrolled in a PhD degree program in Computer Science, EECS, Robotics, Machine Learning, or a closely related quantitative field
- Research Track Record: Publications or submissions in top-tier machine learning or robotics venues (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, RSS, ICRA, CoRL)
- Generative World Models / Video Generation / Diffusion Models
- Vision-Language-Action (VLA) models or embodied AI
- Reinforcement Learning (RL), trajectory optimization, or decision-making systems
More about this role
Atoms is building the machines that power the next era of progress.
Over the last decade, software has transformed the digital world. But the physical world, where food is made, minerals are mined, goods are moved, and industries are run, remains far less intelligent, far less efficient, and far more constrained. We’re changing that.
Atoms builds Physical AI real-world robots for the industries that move civilization forward, starting with food, mining, and transport. Our systems are designed to understand, predict, and control the real world with precision, turning complex physical operations into something more reliable, more scalable, and more productive.
This work requires more than robotics. It requires deep integration across hardware, software, AI, operations, manufacturing, and real estate. We don’t just build machines in a lab. We deploy them into real environments, operate them, learn from them, and improve them until they work at scale.
We are roboticists, engineers, operators, and builders. We believe the next great technology companies will not only transform information, but the physical systems that shape everyday life.
If you want to work on hard problems with...
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