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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Engineer

QuantumLeap Dynamics
San Francisco
Estimated Salary
USD 175.000 – USD 250.000
Live Update
29 Juni 2026
Deadline
29 Jun 2027

Job Description

Join QuantumLeap Dynamics at the forefront of 2026's AI revolution! We're pioneering autonomous systems that will redefine human-machine interaction. As an AI Research Engineer, you'll architect next-gen neural networks for our flagship Quantum OS platform. This role offers unparalleled opportunity to shape the future of technology while working with Nobel Prize-winning researchers in our state-of-the-art downtown San Francisco lab.

We offer competitive equity packages, unlimited PTO, and a wellness stipend. Our culture values intellectual curiosity and bold innovation—perfect for visionaries who thrive at the intersection of quantum computing and artificial intelligence.

Responsibilities

  • Develop novel reinforcement learning algorithms for autonomous decision-making systems
  • Design scalable neural architectures for real-time quantum-AI hybrid processing
  • Lead cross-functional prototyping sessions with robotics and quantum hardware teams
  • Publish breakthrough research in top-tier AI conferences (NeurIPS, ICML)
  • Mentor junior engineers in ethical AI development practices
  • Optimize models for deployment on quantum computing substrates
  • Collaborate with product teams to translate research into commercial applications

Qualifications

  • PhD in Machine Learning, Computer Science, or Quantum Physics (or equivalent experience)
  • 5+ years developing production-level deep learning systems
  • Expertise in PyTorch/TensorFlow with distributed training frameworks
  • Published research in top-tier AI/quantum journals
  • Proficiency in C++ and low-level system optimization
  • Strong background in quantum computing fundamentals
  • Experience with MLOps and CI/CD for ML pipelines
  • Demonstrated ability to lead complex technical projects from concept to deployment

Required Skills

Machine Learning Quantum Computing PyTorch TensorFlow Reinforcement Learning Neural Networks C++ Distributed Systems MLOps Quantum Algorithms

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