ML/AI Research Internship, PhD

About the Role

• Seeking a visionary AI/ML Research Intern to join the founding team.
• Bridge the gap between high-level AI research and real-world physical actuation for next-generation autonomous transport platforms.
• Contribute to core research initiatives on World Models and Vision-Language-Action (VLA) models.
• Translate theoretical breakthroughs into scalable, real-world deployment on physical autonomous systems.
• Drive how platforms perceive, simulate, and navigate complex environments.

Key Responsibilities

• Research and develop cutting-edge RL and distillation techniques for trajectory planning.
• Integrate emerging research from the broader AI community, identifying and prototyping 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 for re-simulation and trajectory querying.
• Ensure core autonomous driving models 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 performance bottlenecks.

Requirements & Tech Stack

• 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 (at least one domain):
• 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 in autonomous driving, mobile robotics, or spatial AI.

This role is based onsite in our Mountain View or San Francisco office.

Hourly pay range: $70/hr.