Job Description
Are you ready to build the infrastructure for the future?
Nexus Horizon Systems is seeking a visionary Lead AI Infrastructure Architect to lead our 2026 roadmap. In an era defined by exponential technological growth, you will be responsible for designing scalable, secure, and high-performance computing ecosystems that power the next generation of Artificial General Intelligence (AGI).
As a key player in our 2026 initiative, you will bridge the gap between theoretical neural architectures and physical hardware capabilities. You will work at the bleeding edge of quantum computing integration, edge AI deployment, and sustainable data center operations.
Why Join Us?
- Work on mission-critical projects that define the technological landscape of 2026 and beyond.
- Competitive compensation package with equity options.
- Flexible hybrid work model in the heart of San Francisco.
Responsibilities
- Architect Next-Gen Systems: Design and implement resilient infrastructure pipelines capable of handling petabyte-scale neural network training and inference.
- Quantum-Ready Optimization: Develop software stacks that seamlessly integrate classical AI models with upcoming quantum processing units (QPUs).
- Latency Reduction: Oversee the deployment of edge computing architectures to minimize latency for real-time AI applications in 2026.
- Security & Compliance: Spearhead post-quantum cryptography implementations to ensure data integrity against evolving cyber threats.
- Team Leadership: Mentor a team of senior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Cost Efficiency: Optimize cloud and on-premise resource allocation to achieve maximum compute efficiency and sustainability.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Electrical Engineering, or a related field with a focus on High-Performance Computing (HPC).
- Experience: Minimum of 8+ years of experience in systems architecture, with at least 3 years in a leadership role overseeing large-scale AI infrastructure.
- Technical Skills: Deep expertise in Python, C++, Rust, and Kubernetes. Proficiency in cloud platforms (AWS, GCP, or Azure).
- AI Knowledge: Strong understanding of deep learning frameworks (TensorFlow, PyTorch) and their underlying hardware optimizations.
- Problem Solving: Demonstrated ability to troubleshoot complex system bottlenecks in distributed environments.
- Communication: Exceptional verbal and written communication skills, capable of articulating complex technical concepts to diverse stakeholders.