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Senior AI Research Scientist 2026

QuantumLeap Labs
San Francisco
Estimated Salary
USD 190.000 – USD 280.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Join QuantumLeap Labs at the forefront of technological evolution as we pioneer solutions for 2026 and beyond. We're seeking a visionary Senior AI Research Scientist to architect next-generation artificial intelligence systems that will reshape industries. Collaborate with world-class teams in our state-of-the-art San Francisco lab to develop quantum-resistant algorithms, autonomous decisioning frameworks, and neural-network architectures for the decade ahead. This role offers unparalleled opportunity to shape the future of human-AI symbiosis.

Responsibilities

  • Design and implement novel deep learning architectures for 2026-era applications
  • Lead cross-functional research on quantum-safe AI protocols and ethical governance frameworks
  • Develop predictive models for complex socio-technical systems with 10+ year forecasting horizons
  • Collaborate with hardware teams to optimize AI systems for next-gen quantum processors
  • Author breakthrough research publications for top-tier AI conferences and journals
  • Mentor junior researchers in emerging AI paradigms and responsible AI practices

Qualifications

  • PhD in Computer Science, AI, or related field with 5+ years industry experience
  • Published research in top-tier AI conferences (NeurIPS, ICML, ICLR) since 2020
  • Expertise in transformer architectures, reinforcement learning, and federated learning
  • Proven track record of deploying production-scale AI systems at scale
  • Strong programming skills in Python, PyTorch/TensorFlow, and high-performance computing
  • Deep understanding of quantum computing principles and their implications for AI
  • Experience with ethical AI frameworks and bias mitigation in complex systems

Required Skills

AI Research Deep Learning Quantum Computing Machine Learning Neural Networks PyTorch TensorFlow Reinforcement Learning Ethics in AI Predictive Modeling

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