Job Description
Join QuantumLeap Dynamics at the forefront of technological evolution. We're seeking a visionary AI Futurist to architect the next generation of intelligent systems that will redefine industries by 2026. This role combines cutting-edge AI research with strategic foresight to develop solutions that anticipate market shifts and human needs. You'll collaborate with Nobel laureates, lead global innovation workshops, and shape the ethical framework for autonomous systems that will power the next decade.
Our Austin campus features a state-of-the-art Neuro-Simulation Lab and partners with MIT's FutureTech Initiative. This hybrid role offers 4-day workweeks, unlimited R&D budget, and equity in our patent portfolio. If you dream in neural networks and have a roadmap for human-AI symbiosis, this is your legacy-defining moment.
Responsibilities
- Design and deploy predictive AI models forecasting technological convergence points for 2026-2030
- Lead cross-functional teams in developing ethical frameworks for autonomous decision systems
- Produce annual 'Horizon Scans' identifying disruptive technologies in quantum computing and synthetic biology
- Architect human-AI co-creation platforms for Fortune 500 transformation initiatives
- Present innovation roadmaps to C-suite executives and government technology councils
- Secure $5M+ annual R&D funding through white papers and investor pitch decks
- Mentor PhD fellows in our Future Leaders Fellowship program
Qualifications
- PhD in AI, Cognitive Science, or Futurism with 8+ years industry experience
- Published research in Nature/Science on AI ethics or human-computer interaction
- Proven track record leading teams that delivered patented AI systems
- Expertise in quantum machine learning and neuromorphic computing
- Deep understanding of ISO/IEC 24027 ethical AI standards
- Portfolio demonstrating strategic tech roadmaps implemented in production
- Experience securing DoD/EU Horizon Europe grants for future-tech projects
- Fluency in Python, TensorFlow, and predictive modeling frameworks