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AI Research Scientist - 2026

Nexus Innovations
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
29 Juni 2026
Deadline
29 Jun 2027

Job Description

Shape the future at Nexus Innovations, where we're pioneering breakthroughs for 2026 and beyond. We're seeking an AI Research Scientist to lead transformative projects in quantum computing, neural interfaces, and autonomous systems. Join our elite team at the forefront of technological evolution and contribute to solutions that will redefine humanity's relationship with technology.

Our state-of-the-art San Francisco lab offers unparalleled resources for experimentation and collaboration. You'll work alongside Nobel laureates and industry disruptors in an environment that encourages bold thinking and rapid prototyping. We offer competitive equity packages, flexible work arrangements, and opportunities to present at global tech summits.

Responsibilities

  • Design and implement novel AI algorithms for next-generation autonomous systems
  • Lead cross-functional research initiatives in quantum machine learning
  • Develop scalable neural interface prototypes for human-AI symbiosis
  • Collaborate with hardware engineers to optimize AI/edge computing architectures
  • Publish breakthrough research in top-tier journals and conferences
  • Mentor junior researchers and drive innovation sprints
  • Secure $2M+ in annual research grants from federal and private entities

Qualifications

  • PhD in Computer Science, AI, or related field with 5+ years industry experience
  • Published 10+ papers in NeurIPS/ICML/ICLR or equivalent tier-1 venues
  • Expertise in transformer architectures, reinforcement learning, and generative models
  • Proficiency in PyTorch/TensorFlow and distributed computing frameworks
  • Experience with quantum computing APIs (Qiskit, Cirq) or neuromorphic hardware
  • Track record of translating research into commercial products
  • Strong background in ethics and alignment frameworks for advanced AI systems

Required Skills

Machine Learning Quantum Computing Neural Interfaces Reinforcement Learning Distributed Systems PyTorch Research Ethics

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