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Information Technology 🏢 Full Time ⭐️ Verified

AI Futurist Strategist

QuantumLeap Labs
Austin
Estimated Salary
USD 175.000 – USD 220.000
Live Update
11 Mei 2026
Deadline
11 Mei 2027

Job Description

Join QuantumLeap Labs as we architect the technological landscape of 2026. We're seeking an AI Futurist Strategist to pioneer breakthrough solutions at the intersection of artificial intelligence and sustainable innovation. This visionary role will shape our R&D roadmap, driving initiatives that redefine human-machine collaboration and ethical AI deployment. Collaborate with Nobel laureates and disrupt industry standards while working in our state-of-the-art Austin hub.

Responsibilities

  • Develop 2026 technology roadmaps for AI integration across healthcare, climate science, and autonomous systems
  • Lead cross-functional teams in prototyping next-gen neural networks with quantum computing capabilities
  • Establish ethical frameworks for AI deployment ensuring alignment with UN Sustainable Development Goals
  • Partner with policymakers to shape regulatory standards for emerging AI technologies
  • Drive innovation sprints targeting carbon-negative AI infrastructure solutions
  • Present strategic vision to C-suite executives and external stakeholders including venture capital partners

Qualifications

  • PhD in Computer Science, AI, or Futuristic Studies with 5+ years applied research experience
  • Published work in Nature/Science journals on AI ethics or quantum machine learning
  • Expertise in generative adversarial networks and federated learning architectures
  • Proven track record scaling AI initiatives from concept to commercial deployment
  • Certification in AI governance frameworks (e.g., EU AI Act compliance)
  • Fluency in Python, TensorFlow/Keras, and quantum programming languages
  • Demonstrated ability to translate complex technical concepts for non-technical stakeholders

Required Skills

AI Ethics Quantum Computing Machine Learning Strategic Foresight Neural Networks Python TensorFlow Federated Learning

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