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AI/ML Engineer - 2026 Future Tech

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

Job Description

Join QuantumLeap Innovations at the forefront of technological evolution! We're seeking visionary AI/ML Engineers to architect intelligent systems that will redefine industries by 2026. Our Austin-based hub combines cutting-edge research with real-world implementation in autonomous systems, quantum computing interfaces, and next-gen human-AI collaboration. Work alongside Nobel laureates and open-source pioneers in an environment where your ideas shape tomorrow's reality.

Responsibilities

  • Design and implement production-grade ML pipelines for predictive analytics and autonomous decision-making
  • Develop novel algorithms for quantum-ML hybrid computing architectures
  • Create ethical AI frameworks for human-AI symbiosis in 2026+ use cases
  • Lead R&D in neuromorphic computing and brain-computer interfaces
  • Collaborate with quantum physicists to develop AI models optimized for quantum processors
  • Architect secure federated learning systems for cross-industry data collaboration
  • Pioneer explainable AI techniques for regulatory compliance in autonomous systems

Qualifications

  • PhD in Computer Science, AI, or Quantum Computing with 3+ years industry experience
  • Expertise in TensorFlow/PyTorch with demonstrated impact on production-scale models
  • Published research in top-tier AI/ML conferences (NeurIPS, ICML) or quantum computing journals
  • Proficiency in quantum programming languages (Qiskit, Cirq) and quantum circuit optimization
  • Strong background in ethical AI frameworks and responsible development practices
  • Experience with neuromorphic computing hardware (Loihi, Intel's Pohoiki)
  • Proven ability to translate theoretical research into commercial applications

Required Skills

Python TensorFlow PyTorch Quantum Computing Neuromorphic Systems Federated Learning Explainable AI NLP Computer Vision Quantum Machine Learning

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