AI Research Scientist

Why Robo Force

Robo Force is an AI robotics company developing Physical AI-powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.

• Design and deploy vision-language(-action) models (VLM/VLA) for contextual understanding and generalized robot action policies.
• Develop and train world models for action-conditioned prediction, long-horizon planning, and environment simulation - enabling robots to reason about the consequences of their actions before execution.
• Research approaches to improve world model fidelity using multi-modal inputs including vision, language, proprioception, and spatial representations.
• Develop foundation models with spatial reasoning capabilities to achieve high-precision robotic actions.
• Integrate multi-modal data sources (vision, language, speech, etc.) to enable natural human-robot communication.
• Optimize and deploy models as production-grade solutions on Robo Force robotic platforms.
• Requires 5 days/week in-office collaboration with the teams.

• PhD degree in Machine Learning, Robotics, or related field, or Master's degree with 4+ years of relevant experience.
• Proficiency in Python and deep learning frameworks (e.g., PyTorch, JAX).
• Expertise in large foundation models (VLM, VLA, etc.).
• Strong understanding of world model architectures and action-conditioned generative modeling for robot learning.
• Decent understanding of multimodal models, modern ML architectures (transformers, diffusion models, etc.).

• Experience with video generation or prediction models (e.g., diffusion-based video models, autoregressive video transformers) and their application to world modeling or synthetic data generation for robot learning.
• Strong publication record at top conferences (NeurIPS, ICML, CVPR, ICCV, CoRL, ICRA, or equivalent).
• Expertise in neural network deployment (e.g., TensorRT) and GPU programming with CUDA.
• Proven ability to design scalable experimentation and data pipelines.

• Competitive stock options/equity programs.
• Health, dental, and vision insurance, 401(k) plan.
• Visa sponsorship and green card support for qualified candidates.
• Lunches and dinners, a fully stocked kitchen, and regular team-building events.