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Information Technology 🏢 Full Time ⭐️ Verified

Senior AI Research Scientist

QuantumLeap Dynamics
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
19 Mei 2026
Deadline
19 Mei 2027

Job Description

Join QuantumLeap Dynamics at the forefront of technological revolution as we pioneer breakthroughs in artificial intelligence for 2026 and beyond. Our Austin-based innovation hub is seeking a visionary Senior AI Research Scientist to develop next-generation machine learning models that will redefine industries. You'll collaborate with world-class engineers in an agile, future-focused environment where your expertise directly shapes tomorrow's solutions.

We offer competitive equity packages, unlimited learning stipends, and a state-of-the-art research lab equipped with quantum computing infrastructure. Your work will directly impact Fortune 500 partners in healthcare, climate tech, and autonomous systems.

Responsibilities

  • Design and implement novel deep learning architectures for multi-modal data processing
  • Lead research initiatives in quantum-enhanced machine learning algorithms
  • Develop production-ready AI systems with 99.99% reliability metrics
  • Mentor cross-functional teams in AI best practices and ethical frameworks
  • Publish breakthrough research in top-tier AI conferences and journals
  • Collaborate with product teams to translate research into commercial solutions

Qualifications

  • PhD in Computer Science, Machine Learning, or related field with 5+ years industry experience
  • Proven track record of publishing in NeurIPS/ICML/ICLR or equivalent tier conferences
  • Expertise in transformer architectures, reinforcement learning, and distributed ML systems
  • Proficiency with PyTorch/TensorFlow and cloud-based ML deployment (AWS/GCP)
  • Strong background in ethical AI development and bias mitigation strategies
  • Experience leading technical teams and managing research roadmaps

Required Skills

Machine Learning Deep Learning PyTorch TensorFlow Quantum Computing Research AI Ethics Distributed Systems Reinforcement Learning

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