Job
Description
About QpiAI works at the intersection of AI and Quantum Computing, developing groundbreaking solutions to tackle some of the most complex challenges across various industries. Our team is dedicated to pushing the boundaries of innovation and delivering exceptional results to our enterprise clients, driving significant impact. Key Responsibilities Design and implement novel multi-agent architectures that enable complex problem-solving and collaboration Develop domain-specific scaffolding techniques to guide AI systems in specialized environments Create and curate high-quality datasets for training and evaluating AI systems across various domains Establish comprehensive evaluation frameworks to measure system performance, robustness, and alignment Research and implement reinforcement learning approaches for agent behavior optimization Develop techniques for post-training optimization and domain adaptation Ideal Profile Advanced degree (MS or PhD) in Computer Science, Machine Learning, AI, or related technical field 4+ years of experience in applied AI research Strong mathematical foundations in optimization, probability theory, and linear algebra Extensive expertise in Python programming and software development Strong proficiency in PyTorch or JAX for implementing and scaling AI models Experience in curating datasets, pre-training, and aligning multimodal small language models Nice to Have Publication record in top-tier AI/ML conferences (NeurIPS, ICLR, AAMAS, ICML) Experience applying AI to scientific domains such as drug discovery, materials science, chip design, or mathematical reasoning Experience working with vision-language models and multimodal architectures Skills: Python,Software Development,PyTorch,JAX,Reinforcement Learning,Multi-Agent Systems,Dataset Curation,Model Evaluation,Optimization,Probability Theory,Linear Algebra Show more Show less
