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Information Technology 🏢 Full Time ⭐️ Verified

AI/ML Engineer - Futuristic Tech Solutions

Nexus Innovations Inc.
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
13 Mei 2026
Deadline
13 Mei 2027

Job Description

Join Nexus Innovations at the forefront of 2026's technological revolution. We're pioneering quantum-entangled AI systems that will redefine human-machine interaction. As an AI/ML Engineer, you'll architect neural networks capable of predictive cognition and autonomous decision-making in real-world environments. Our cross-disciplinary team combines quantum computing, bio-inspired algorithms, and edge-optimized neural processing to create sentient-level AI solutions. Work with bleeding-edge hardware including photonic processors and neuromorphic chips while contributing to projects that will shape humanity's digital future.

Responsibilities

  • Design and implement quantum-enhanced machine learning models for predictive analytics
  • Develop autonomous decision-making frameworks using reinforcement learning
  • Optimize neural networks for edge deployment on quantum processors
  • Create bio-inspired algorithms mimicking human cognition patterns
  • Architect scalable AI infrastructure supporting real-time data streams
  • Implement federated learning protocols for distributed quantum networks
  • Develop ethical AI governance frameworks aligned with 2026 regulatory standards

Qualifications

  • PhD in Computer Science/AI or equivalent with 5+ years in quantum ML
  • Expertise in TensorFlow Quantum, Qiskit, and Cirq frameworks
  • Published research in Nature/Science on quantum neural networks
  • Proficiency in neuromorphic computing (Loihi, Intel's Lohi)
  • Experience with photonic computing systems (Lightmatter, Xanadu)
  • Strong background in ethical AI and bias mitigation
  • Portfolio of deployed AI systems handling 1M+ concurrent operations

Required Skills

Quantum Machine Learning TensorFlow Quantum Qiskit Neuromorphic Computing Photonic Processing Reinforcement Learning Federated Learning AI Ethics

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