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Information Technology 🏢 Full Time ⭐️ Verified

AI Systems Architect 2026

QuantumLeap Dynamics
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
24 Mei 2026
Deadline
24 Mei 2027

Job Description

Shape the future of artificial intelligence at QuantumLeap Dynamics. We're seeking a visionary AI Systems Architect to design next-generation machine learning frameworks that will redefine industries by 2026. Join our elite R&D team in Austin's thriving tech ecosystem and architect solutions for quantum-optimized neural networks, ethical AI governance systems, and autonomous decisioning platforms.

This hybrid role offers unmatched opportunities to collaborate with Nobel laureates and pioneer breakthroughs in AGI safety, neuromorphic computing, and climate modeling AI. We provide cutting-edge resources, unlimited PTO, and equity in a company valued at $2B+.

Responsibilities

  • Design scalable AI architectures supporting 10M+ concurrent inference operations
  • Develop ethical AI governance frameworks compliant with 2026 global standards
  • Lead quantum-neural hybrid system implementations using D-Wave hardware
  • Create real-time anomaly detection systems for critical infrastructure
  • Optimize ML pipelines for edge-to-cloud deployment at 99.99% uptime
  • Mentor cross-functional teams in advanced MLops methodologies
  • Patent 3+ breakthrough AI technologies annually

Qualifications

  • PhD in Computer Science/AI with 8+ years production ML experience
  • Expertise in transformer architectures, reinforcement learning, and federated learning
  • Published research in top-tier AI conferences (NeurIPS/ICML) since 2020
  • Proficiency in TensorFlow/PyTorch with CUDA optimization
  • Proven track record deploying AI systems at hyperscale (AWS/GCP/Azure)
  • Strong background in AI ethics and bias mitigation frameworks
  • Certified in quantum computing fundamentals (IBM/Qiskit preferred)

Required Skills

AI Architecture Machine Learning Quantum Computing Ethics in AI TensorFlow PyTorch Reinforcement Learning Distributed Systems

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