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AI Systems Architect (2026)

QuantumLeap Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
22 Mei 2026
Deadline
22 Mei 2027

Job Description

Join QuantumLeap Dynamics at the forefront of technological evolution as we build the AI infrastructure that will define 2026. We're seeking a visionary AI Systems Architect to design and implement next-generation neural networks that will power autonomous systems, predictive analytics, and human-AI collaboration platforms. This role sits at the intersection of quantum computing, edge AI, and ethical machine learning, offering unparalleled opportunities to shape the future of intelligent systems.

In our state-of-the-art San Francisco lab, you'll work alongside Nobel-caliber researchers and industry pioneers to develop scalable AI architectures capable of processing petabytes of real-time data while maintaining quantum-grade security. We offer comprehensive benefits including equity, flexible work arrangements, and a $20,000 annual innovation stipend.

Responsibilities

  • Design quantum-resistant neural architectures for 2026-era autonomous systems
  • Lead development of edge-optimized AI inference engines with sub-5ms latency
  • Implement federated learning frameworks for cross-organizational data collaboration
  • Create ethical AI governance protocols aligned with 2026 regulatory standards
  • Architect multimodal AI systems integrating vision, language, and sensor fusion
  • Develop real-time anomaly detection systems for critical infrastructure protection
  • Optimize AI training pipelines for quantum-accelerated computing environments

Qualifications

  • PhD in Computer Science, AI, or Quantum Computing (or equivalent experience)
  • Expertise in transformer architectures and quantum machine learning algorithms
  • 5+ years building production-grade AI systems with 99.99% uptime SLAs
  • Proficiency in PyTorch, TensorFlow Quantum, and C++ for high-performance systems
  • Published research in top-tier AI/ML conferences (NeurIPS, ICML, ICLR)
  • Deep understanding of federated learning and differential privacy techniques
  • Experience with MLOps pipelines for distributed AI training at scale

Required Skills

AI Architecture Quantum Computing Neural Networks Federated Learning MLOps PyTorch TensorFlow Quantum C++ Edge AI Anomaly Detection Ethical AI Multimodal Systems

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