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Information Technology 🏢 Full Time ⭐️ Verified

Lead AI Engineer - 2026 Vision

Nexus Innovations Inc.
San Francisco
Estimated Salary
USD 180.000 – USD 220.000
Live Update
17 Mei 2026
Deadline
17 Mei 2027

Job Description

Join Nexus Innovations Inc. at the forefront of technological evolution as we pioneer solutions for 2026 and beyond. We're seeking a visionary Lead AI Engineer to architect and deploy next-generation artificial intelligence systems that will redefine industries. This role offers unparalleled opportunity to shape the future of human-machine interaction while collaborating with world-class researchers and engineers in our state-of-the-art San Francisco hub.

Responsibilities

  • Design and implement scalable AI/ML frameworks for autonomous systems and predictive analytics
  • Lead cross-functional teams in developing ethical AI solutions aligned with 2026 technological standards
  • Optimize neural networks and deep learning models for real-time processing at edge devices
  • Conduct advanced research in quantum-AI hybrid computing and neuromorphic systems
  • Develop robust data pipelines for multi-modal sensor fusion and contextual intelligence
  • Drive innovation in explainable AI (XAI) and federated learning architectures
  • Mentor junior engineers while establishing best practices for AI governance and compliance

Qualifications

  • PhD in Computer Science/AI or equivalent with 8+ years of progressive experience
  • Expertise in PyTorch/TensorFlow with production deployment of large-scale models (>1B parameters)
  • Published research in top-tier AI conferences (NeurIPS, ICML, ICLR) or equivalent industry impact
  • Proficiency in quantum computing frameworks (Qiskit, Cirq) and neuromorphic engineering
  • Strong background in ethics, bias mitigation, and responsible AI development
  • Experience leading teams in agile environments with CI/CD for ML systems
  • Fluency in Python, C++, and distributed computing frameworks (Spark, Kubernetes)

Required Skills

AI/ML PyTorch TensorFlow Quantum Computing Neuromorphic Engineering Explainable AI Federated Learning Quantum-AI Hybrid Systems MLOps Ethics in AI Edge Computing Data Pipelines

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