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Lead AI Architect: Project 2026

Apex Quantum Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
Live Update
28 Juni 2026
Deadline
28 Jun 2027

Job Description

Join the Vanguard of Tomorrow.

Apex Quantum Dynamics is seeking a visionary Lead AI Architect to spearhead Project 2026. This is a unique opportunity to define the technological roadmap for next-generation neural networks and quantum-assisted computing systems. You will bridge the gap between theoretical AI research and scalable production infrastructure.

In this high-impact role, you will guide a team of elite engineers in developing autonomous systems that are resilient, intelligent, and future-proof. If you are passionate about pushing the boundaries of what is possible in Artificial Intelligence and Hardware Integration, we want to hear from you.

Responsibilities

  • Architect and implement scalable AI/ML infrastructures capable of handling petabyte-scale data processing.
  • Lead the research and integration of quantum computing algorithms into classical neural networks.
  • Mentor and develop senior engineers, fostering a culture of technical excellence and innovation.
  • Collaborate with cross-functional R&D teams to define technical roadmaps for Project 2026.
  • Optimize model latency and inference accuracy for real-time deployment environments.
  • Establish best practices for code quality, security, and deployment automation.

Qualifications

  • PhD or Master’s degree in Computer Science, Mathematics, or a related technical field.
  • 10+ years of experience in software engineering, with at least 5 years in AI/ML architecture.
  • Expert proficiency in Python, TensorFlow, PyTorch, and C++.
  • Deep understanding of distributed systems, cloud architecture (AWS/GCP), and Kubernetes.
  • Experience with quantum computing frameworks (e.g., Qiskit, Cirq) is a significant plus.
  • Strong track record of leading high-performing engineering teams through complex technical challenges.

Required Skills

Python Machine Learning Deep Learning Cloud Architecture Kubernetes Docker Quantum Computing C++ TensorFlow PyTorch System Design AWS GCP

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