Artificial Intelligence Engineer

We are on a mission to revolutionize home automation through AI-powered robotics. Our team brings deep expertise in artificial intelligence, machine learning, and robotics, with experience building some of the most advanced autonomous systems in the world. With strong financial backing and a vision to transform everyday life, we are developing cutting-edge AI systems that enable robots to assist with real-world household tasks.The RoleWe are looking for an AI Researcher/Engineer specializing in large-scale pretraining, scaling, and post-training of large language models (LLMs) and Vision-Language Models (VLMs). In this role, you will conduct fundamental research into model architecture, reasoning, and training efficiency to enhance the intelligence of next-generation autonomous robots. You will work closely with a world-class team of engineers and researchers to push the boundaries of AI-driven robotics.What You'll DoConduct cutting-edge research on pretraining and scaling LLMs and VLMs for robotic applications.Design and implement novel architectures for multi-modal learning, reasoning, and adaptation.Develop efficient distributed training pipelines and model scaling strategies.Optimize post-training techniques such as supervised fine-tuning and reinforcement learning (e.g., RLHF, PPO).Collaborate with engineering teams to integrate AI models into real-world robotic systems.Stay ahead of advancements in deep learning, generative AI, and reinforcement learning.Contribute to academic research, patents, and technical documentation.What We're Looking ForPhD or Master's degree in Computer Science, AI, or a related field with strong deep learning research experience.Expertise in pretraining and scaling LLMs/VLMs with experience in large-scale training.Strong knowledge of transformers, attention mechanisms, and multi-modal learning.Hands-on experience with distributed training frameworks (PyTorch, TensorFlow, JAX, DeepSpeed, Megatron-LM).Proficiency in Python and deep learning libraries.Experience with post-training techniques such as reinforcement learning and fine-tuning.Familiarity with cloud-based training (AWS, GCP, Azure) and high-performance computing.Bonus PointsExperience in applying LLMs and VLMs to robotics or autonomous systems.Research publications in NeurIPS, ICML, ICLR, or similar conferences.Background in multi-modal AI (vision, language, and sensor fusion).Knowledge of MLOps, model deployment, and inference optimization.

Created: 2025-03-01