Job Description
Join Nexus Innovations at the forefront of 2026's technological revolution. We're seeking a visionary AI Futurist Strategist to architect tomorrow's intelligent systems. As a pioneer in quantum-adjacent AI, you'll transform theoretical breakthroughs into practical solutions that redefine human-machine collaboration. Our multidisciplinary team operates at the intersection of neuroscience, computational linguistics, and emergent technologies. This role offers unparalleled access to our $500M R&D lab and partnership with MIT's Future Intelligence Initiative.
Shape the future of artificial consciousness while enjoying industry-leading benefits: equity grants, unlimited learning stipends, and flexible remote work from our biophilic campuses. If you dream in algorithms and breathe innovation, this is your calling.
Responsibilities
- Lead cross-functional R&D initiatives in generative AI and quantum computing integration
- Develop ethical frameworks for autonomous systems with embedded moral reasoning
- Architect neural-symbolic AI architectures combining deep learning with knowledge graphs
- Partner with Stanford's Human-AI Interaction Lab on next-gen interfaces
- Present breakthroughs at global tech summits including Web3.0 Expo and AI Decoded
- Mentor PhD fellows in our Future Intelligence Fellowship program
- Drive patent portfolio expansion in AI explainability and federated learning
Qualifications
- PhD in AI, Cognitive Science, or Quantum Computing with 5+ years industry experience
- Published research in Nature Machine Intelligence or top-tier AI conferences
- Expertise in transformer architectures, reinforcement learning, and neuromorphic computing
- Proficiency in Python, PyTorch, and quantum programming languages (Q# or Qiskit)
- Proven track record of deploying production-grade AI systems at scale
- Deep understanding of ethical AI governance frameworks (EU AI Act, NIST RMF)
- Experience with neuromorphic hardware (IBM TrueNorth, Intel Loihi)
- Portfolio demonstrating work on human-centered AI or explainable systems