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Information Technology 🏢 Full Time ⭐️ Verified

Senior AI/ML Engineer

QuantumLeap Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
15 Mei 2026
Deadline
15 Mei 2027

Job Description

Join QuantumLeap Dynamics as a Senior AI/ML Engineer to architect the next generation of intelligent systems for 2026 and beyond. We're pioneering breakthroughs in autonomous AI, quantum-optimized machine learning, and neural-network-on-chip integration. This role offers unparalleled opportunity to shape the future of human-AI collaboration in our state-of-the-art innovation lab.

What you'll achieve: Design production-ready ML pipelines, lead cross-functional quantum-AI research initiatives, and mentor a team of brilliant engineers solving humanity's grand challenges. Our culture blends Silicon Valley agility with academic rigor, offering flexible work arrangements, cutting-edge resources, and equity in a rapidly scaling unicorn.

Responsibilities

  • Architect scalable ML systems using quantum-accelerated frameworks (PyTorch, TensorFlow, Qiskit)
  • Lead development of autonomous AI agents for real-world decision-making
  • Pioneer neural-network-on-chip (NoC) integration for edge computing
  • Drive ethical AI governance frameworks for 2026 compliance standards
  • Mentor junior engineers in advanced ML paradigms (diffusion models, transformer variants)
  • Publish research in top-tier AI journals/conferences (NeurIPS, ICML)
  • Collaborate with quantum computing teams for hybrid algorithm development

Qualifications

  • MS/PhD in Computer Science, AI, or Quantum Computing with 5+ years industry experience
  • Expertise in Python/C++ with production deployment of ML systems (>10M users)
  • Published research in quantum machine learning or neural architecture search
  • Proven experience with distributed ML frameworks (Ray, Horovod)
  • Deep understanding of quantum computing principles (QAOA, VQE)
  • Strong background in ethical AI bias mitigation and explainability techniques
  • Portfolio showcasing 3+ production AI systems with measurable impact
  • Experience with MLOps pipelines (MLflow, Kubeflow) and cloud-native deployment

Required Skills

Python TensorFlow PyTorch Quantum Computing Neural Networks Machine Learning Deep Learning AI Ethics MLOps Distributed Systems Qiskit

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