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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist (2026 Vision)

Nexus Labs
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
New
Live Update
1 Juni 2026
Deadline
1 Jun 2027

Job Description

Join Nexus Labs at the forefront of technological evolution as we pioneer AI solutions for 2026. We're seeking visionary researchers to architect next-generation neural networks that will redefine human-machine interaction. Our interdisciplinary team operates at the intersection of quantum computing, biometric AI, and predictive analytics to solve humanity's most complex challenges.

In this role, you'll collaborate with Nobel laureates and industry disruptors to develop autonomous systems with ethical guardrails. We offer competitive equity packages, unlimited R&D budgets, and a lab equipped with exascale computing resources. Shape the future of artificial intelligence while enjoying San Francisco's vibrant tech ecosystem.

Responsibilities

  • Design and implement cutting-edge AI models for 2026-era applications
  • Lead cross-functional research initiatives in quantum-enhanced machine learning
  • Publish breakthrough findings in top-tier journals and conferences
  • Develop ethical frameworks for autonomous decision-making systems
  • Mentor junior researchers and patent novel AI methodologies
  • Collaborate with product teams to translate research into commercial solutions

Qualifications

  • PhD in Computer Science, AI, or related field with 5+ years industry experience
  • Proven track record of publishing in NeurIPS/ICML/ICLR venues
  • Expertise in transformer architectures, reinforcement learning, and federated learning
  • Proficiency in Python, PyTorch, TensorFlow, and quantum programming frameworks
  • Demonstrated ability to secure federal research grants (NSF/NIH/DARPA)
  • Strong background in AI ethics and bias mitigation techniques
  • Experience with MLOps and production-scale AI deployment

Required Skills

Machine Learning Deep Learning Quantum Computing Neural Networks Python PyTorch TensorFlow Reinforcement Learning AI Ethics MLOps

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