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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist (2026)

Nexus Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
2 Juli 2026
Deadline
2 Jul 2027

Job Description

Join Nexus Labs at the forefront of technological evolution as we pioneer breakthrough AI systems for 2026 and beyond. We're seeking visionary researchers to develop next-generation machine learning models that will redefine industries. Our state-of-the-art facility in San Francisco offers unparalleled resources to transform theoretical concepts into real-world applications.

As an AI Research Scientist, you'll collaborate with Nobel laureates and industry veterans in an environment that values curiosity, innovation, and ethical AI development. We offer competitive compensation, comprehensive benefits, and the opportunity to shape the future of artificial intelligence.

Responsibilities

  • Design and implement novel deep learning architectures for 2026-era applications
  • Lead research initiatives in quantum machine learning and neuromorphic computing
  • Develop ethical frameworks for autonomous systems and generative AI
  • Collaborate with cross-functional teams to prototype and validate AI solutions
  • Publish research in top-tier conferences and journals
  • Secure patents for innovative AI methodologies
  • Mentor junior researchers and foster a culture of scientific excellence

Qualifications

  • PhD in Computer Science, AI, or related field with 3+ years industry experience
  • Expertise in transformer architectures, reinforcement learning, and federated learning
  • Proficiency in PyTorch, TensorFlow, and distributed computing frameworks
  • Publication record at NeurIPS, ICML, or equivalent tier conferences
  • Demonstrated ability to translate research into production-scale systems
  • Strong background in ethics, bias mitigation, and responsible AI
  • Experience with quantum computing simulations or neuromorphic hardware

Required Skills

Deep Learning Quantum Computing Ethics PyTorch Reinforcement Learning Federated Learning Neuromorphic Systems

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