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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist - Future Technologies

QuantumLeap Dynamics
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
28 Juni 2026
Deadline
28 Jun 2027

Job Description

Join QuantumLeap Dynamics at the forefront of technological innovation as we pioneer solutions for 2026 and beyond. We're seeking visionary AI Research Scientists to develop cutting-edge machine learning models that will redefine industries. Our Austin-based hub offers unparalleled resources and collaborative environments where your ideas can transform into reality. Benefit from competitive compensation, flexible work arrangements, and opportunities to publish groundbreaking research in top-tier journals.

Responsibilities

  • Design and implement advanced neural network architectures for next-gen autonomous systems
  • Lead cross-functional teams to translate research prototypes into scalable production models
  • Develop novel algorithms for quantum-classical hybrid computing optimization
  • Collaborate with hardware engineers to co-design AI-accelerated computing platforms
  • Publish high-impact research in peer-reviewed journals and top-tier conferences
  • Secure external funding through government grants and industry partnerships
  • Mentor junior researchers and foster an innovative lab culture

Qualifications

  • PhD in Computer Science, AI, or related field with 3+ years industry experience
  • Expertise in deep learning frameworks (PyTorch, TensorFlow) and distributed computing
  • Proven track record of publishing in NeurIPS/ICML/ICLR or equivalent
  • Strong background in reinforcement learning and generative modeling
  • Experience with quantum computing APIs (Qiskit, Cirq) or neuromorphic systems
  • Proficiency in Python, C++, and high-performance computing environments
  • Demonstrated ability to translate complex research into practical applications

Required Skills

Machine Learning Deep Learning Reinforcement Learning Quantum Computing Python PyTorch TensorFlow Distributed Systems

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