Job Description
We are at the precipice of a new technological era. Quantum Horizon Systems is seeking a visionary Principal AI Architect to lead the development of our core infrastructure for the 2026 technology landscape. If you are passionate about pushing the boundaries of generative AI, autonomous agents, and next-gen neural architectures, we want to meet you.
In this pivotal role, you will define the technical roadmap for our proprietary models, ensuring our solutions remain at the forefront of the industry. You will work directly with C-level leadership to bridge the gap between theoretical AI research and scalable production systems.
Why Join Us?
- Work on projects that define the future of AI.
- Competitive equity package and performance bonuses.
- Flexible remote-first culture with premium San Francisco office access.
Responsibilities
- Architect 2026-Ready Systems: Design and oversee the deployment of large-scale generative models, focusing on multimodal capabilities and agentic workflows.
- Research & Development: Lead internal research initiatives to explore emerging paradigms in Transformer optimization and quantum-inspired algorithms.
- Performance Optimization: Engineer high-throughput inference pipelines capable of handling millions of requests per second with minimal latency.
- Team Leadership: Mentor a team of senior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Strategic Alignment: Collaborate with product and engineering teams to translate 2026 roadmap objectives into concrete technical specifications.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field; Ph.D. preferred.
- Experience: 8+ years of experience in machine learning engineering, with at least 3 years in a senior architectural role.
- Technical Skills: Deep expertise in Python, PyTorch, and TensorFlow; proven track record of deploying LLMs (Large Language Models) in production environments.
- 2026 Competencies: Demonstrated experience with RAG (Retrieval-Augmented Generation), fine-tuning strategies, and MLOps best practices.
- Problem Solving: Exceptional ability to solve complex system design problems and scale AI models efficiently.