Job Description
Join Nexus Innovations at the forefront of technological evolution for 2026. We're seeking a visionary Senior AI Research Scientist to architect next-generation artificial intelligence solutions that will redefine industries. As a key architect in our 2026 AI Vision Initiative, you'll collaborate with Nobel laureates and industry pioneers to develop breakthrough neural architectures, quantum-inspired algorithms, and sentient AI prototypes. Our state-of-the-art Quantum Neural Lab offers unparalleled resources for your research.
At Nexus, we don't just adapt to the future—we create it. You'll lead cross-disciplinary teams of physicists, ethicists, and engineers to develop AGI frameworks that balance exponential capability with human-centric values. This role includes dedicated research funding, flexible innovation time, and direct influence on our 2026 product roadmap.
Responsibilities
- Design and implement novel quantum neural networks for 2026-era AI systems
- Lead research on sentient AI architectures with ethical alignment frameworks
- Develop patent-pending algorithms for real-time cognitive processing at exascale
- Mentor junior researchers in next-generation AI paradigms
- Collaborate with ethics councils to build responsible AI governance models
- Present breakthrough findings at global AI summits and publish in Nature/AI journals
- Shape industry standards for 2026 AI safety and transparency protocols
Qualifications
- PhD in Computer Science, Quantum Computing, or related field (or equivalent breakthrough research)
- 5+ years developing production AI systems with demonstrated paradigm-shifting impact
- Expertise in quantum machine learning and neuromorphic computing
- Published research in top-tier AI/quantum journals with 500+ citations
- Proficiency in PyTorch Quantum, TensorFlow Quantum, and custom hardware programming
- Deep understanding of AGI safety alignment and value learning frameworks
- Track record of leading teams that delivered industry-first AI capabilities
- Experience with billion-parameter model optimization at exascale performance