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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist (2026 Vision)

Nexus Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
22 Mei 2026
Deadline
22 Mei 2027

Job Description

Join Nexus Labs at the forefront of 2026's technological revolution! We're pioneering AI systems that will redefine industries by 2026, and we need visionary researchers to shape tomorrow's innovations. Our interdisciplinary team combines quantum computing, neural networks, and sustainable AI to solve humanity's greatest challenges.

This role offers unparalleled resources for groundbreaking research, collaboration with Nobel laureates, and the opportunity to publish in top-tier journals. We provide competitive compensation, flexible work arrangements, and a state-of-the-art lab in the heart of San Francisco's tech ecosystem.

Responsibilities

  • Design and implement advanced AI models for 2026-era applications in healthcare, climate science, and autonomous systems
  • Lead cross-functional teams to translate theoretical research into production-ready solutions
  • Publish peer-reviewed papers and present findings at leading AI conferences
  • Develop ethical frameworks for next-generation AI systems
  • Collaborate with industry partners to prototype 2026 technology roadmaps
  • Mentor junior researchers and foster a culture of innovation

Qualifications

  • PhD in Computer Science, Machine Learning, or related field with 3+ years of research experience
  • Expertise in transformer architectures, reinforcement learning, or quantum machine learning
  • Publication record at NeurIPS/ICML/ICLR with 10+ peer-reviewed papers
  • Proficiency in PyTorch/TensorFlow and distributed computing frameworks
  • Strong background in ethics and responsible AI development
  • Experience with interdisciplinary research (biology, physics, or economics preferred)
  • Proven ability to secure research grants or industry partnerships

Required Skills

AI Research Machine Learning Deep Learning Quantum Computing Neural Networks PyTorch TensorFlow Reinforcement Learning Ethics Scientific Publishing

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