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Senior AI Research Engineer - 2026 Initiative

QuantumLeap Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Join QuantumLeap Dynamics at the forefront of the 2026 Initiative—a groundbreaking project redefining artificial intelligence's future. We're seeking visionary Senior AI Research Engineers to architect next-generation neural networks and deploy scalable machine learning solutions that will transform industries worldwide. This is your chance to shape the AI landscape with cutting-edge research in a collaborative, high-impact environment.

Our team operates at the intersection of quantum computing and deep learning, pushing boundaries in autonomous systems and predictive analytics. You'll collaborate with Nobel laureates and industry pioneers while leveraging our state-of-the-art R&D facilities in the heart of San Francisco's tech corridor.

Responsibilities

  • Design and implement novel AI architectures for quantum-enhanced machine learning models
  • Lead research initiatives in unsupervised learning and neural network optimization
  • Develop production-ready ML pipelines handling petabyte-scale datasets
  • Mentor junior researchers and publish findings in top-tier AI conferences
  • Collaborate with cross-functional teams to deploy AI solutions in autonomous systems
  • Stay current with emerging AI paradigms and quantum computing advancements

Qualifications

  • PhD in Computer Science, Machine Learning, or related field (or equivalent experience)
  • 5+ years developing production AI systems at scale
  • Expertise in deep learning frameworks (PyTorch, TensorFlow) and quantum computing APIs
  • Published research in NeurIPS, ICML, or equivalent tier conferences
  • Proficiency in Python, C++, and distributed computing systems
  • Strong background in reinforcement learning and neural network optimization
  • Experience with cloud AI platforms (AWS SageMaker, GCP AI Platform)

Required Skills

Deep Learning Quantum Computing PyTorch TensorFlow Reinforcement Learning Distributed Systems Python C++ AWS Research Methodology

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