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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist - 2026 Vision

NexusLabs
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
3 Juli 2026
Deadline
3 Jul 2027

Job Description

Join NexusLabs at the forefront of technological evolution as we pioneer solutions for 2026 and beyond. We seek an AI Research Scientist to develop next-generation artificial intelligence systems that will shape human-machine interaction. Our Austin-based innovation hub combines cutting-edge research with real-world application, offering unparalleled opportunities to impact the future of intelligent systems.

Why NexusLabs? We foster a culture of relentless innovation where your ideas become reality. With industry-leading resources and collaborative partnerships with MIT and Stanford, you'll work alongside Nobel laureates and Turing Award winners to solve humanity's most complex challenges.

Responsibilities

  • Design and implement advanced neural network architectures for 2026-era applications
  • Lead breakthrough research in quantum-entangled AI systems and edge computing
  • Develop ethical frameworks for autonomous decision-making in critical infrastructure
  • Pioneer human-AI symbiosis interfaces for healthcare and space exploration
  • Collaborate with governments on AI policy development for 2030 digital governance
  • Publish findings in Nature AI and IEEE Transactions on Neural Networks

Qualifications

  • PhD in Computer Science, AI, or Quantum Computing (or equivalent experience)
  • 5+ years developing production-grade AI systems with 99.99% reliability
  • Published research in top-tier AI/ML conferences (NeurIPS, ICML, ICLR)
  • Expertise in transformer architectures and federated learning
  • Proficiency in quantum programming (Qiskit, Cirq) and neuromorphic computing
  • Strong background in AI ethics and regulatory compliance frameworks
  • Experience with NASA/DOE high-performance computing environments

Required Skills

AI Research Machine Learning Quantum Computing Neural Networks Ethics High-Performance Computing Transformer Models Federated Learning

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