Job Description
The Future is Now. We are QuantumNext Systems, a premier research lab pioneering the intersection of Generative AI and Quantum Computing for the 2026 era. We are seeking a visionary Lead AI Architect to define the next generation of autonomous intelligence systems. If you are passionate about building the infrastructure that will power the world's most advanced neural networks, we want to hear from you.
In this role, you will not just write code; you will architect the cognitive backbone of our future products. You will work directly with our CTO and lead a team of elite engineers to solve complex problems in scalability, ethical alignment, and real-time inference.
Responsibilities
- Design and implement the next generation of Large Language Models (LLMs) optimized for hybrid quantum-classical computing environments.
- Lead a high-performance team of Machine Learning Engineers to push the boundaries of Artificial General Intelligence (AGI) capabilities.
- Architect scalable, fault-tolerant inference engines capable of handling millions of concurrent neural requests.
- Establish and enforce robust ethical guidelines and safety protocols for autonomous decision-making systems.
- Collaborate with cross-functional product teams to integrate AI agents into the emerging spatial computing ecosystem.
- Conduct research and publish findings in top-tier AI conferences to maintain our industry leadership.
Qualifications
- Master’s or PhD degree in Computer Science, Artificial Intelligence, Applied Mathematics, or a related quantitative field.
- 10+ years of professional experience in machine learning engineering, with at least 5 years in a leadership or architectural role.
- Extensive experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Strong proficiency in Python and experience with distributed computing systems (Kubernetes, Docker, Spark).
- Demonstrated track record of publishing in top-tier AI venues (NeurIPS, ICML, ICLR) or delivering commercial-scale AI products.
- Deep understanding of transformer architectures, reinforcement learning, and natural language processing.