ML/AI Research Internship - PhD at Atoms in San Francisco, CA

Job description

Who we are 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 real-world impact, join us. 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. In this role, you will contribute to core research initiatives on World Models and Vision-Language-Action (VLA) models. You will help translate cutting-edge theoretical breakthroughs into scalable, real-world deployment on physical autonomous systems, driving how our platforms perceive, simulate, and navigate complex environments. What you'll do Research and develop cutting-edge RL and distillation techniques for trajectory planning. Integrate emerging research from the broader AI community, identifying and prototyping the most promising solutions. Design and prototype end-to-end multimodal models that translate real-time visual perception and high-level behavioral goals into physical vehicle actuation. Develop interactive world models from raw multi-sensor logs, allowing the team to re-simulate events and query what a vehicle would see if it altered its trajectory. Ensure core autonomous driving models can seamlessly adapt to novel urban environments and edge cases. Partner with validation and research teams to run model experiments through rigorous simulated scenarios, detecting regressions and identifying systemic performance bottlenecks. What we'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). Core Expertise: Graduate-level research background focused on at least one of the following domains: Generative World Models / Video Generation / Diffusion Models Vision-Language-Action (VLA) models or embodied AI Reinforcement Learning (RL), trajectory optimization, or decision-making systems Large-scale multimodal Transformer architectures applied to robotics or spatio-temporal data Framework Fluency: Advanced hands-on proficiency with PyTorch or JAX for training and fine-tuning large-scale neural networks. Programming: Proficiency in Python and working knowledge of C++. Preferred qualifications Hands-on experience with Sensor Fusion integrating inputs across multi-camera setups, LiDAR, and Radar. Familiarity with Bird’s-Eye-View (BEV) visual representations, 3D occupancy forecasting, multi-task learning, or specialized data tokenization for spatial data. Prior experience with large-scale distributed training on GPU/TPU clusters (e.g., DeepSpeed, Megatron-LM, Ray, or PyTorch FSDP). Prior research or internship experience i