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Information Technology 🏒 Full Time ⭐️ Verified

Lead Quantum Interface Architect

Nexus 2026 Technologies
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
11 Mei 2026
Deadline
11 Mei 2027

Job Description

We are seeking a visionary Lead Quantum Interface Architect to join our elite team in San Francisco. At Nexus 2026 Technologies, we are building the foundational infrastructure for the next era of human-computer symbiosis. If you are passionate about merging neural networks with quantum computing and want to define the technology roadmap for 2026 and beyond, this is your opportunity to lead.

Our mission is to accelerate the singularity by creating seamless, low-latency interfaces between biological cognition and digital processing. You will work alongside world-class physicists and engineers to solve the world's most complex computational problems.

Responsibilities

  • Design and architect scalable quantum neural networks capable of processing petabytes of real-time data.
  • Lead the technical strategy for our 2026 roadmap, defining milestones for quantum hardware integration.
  • Collaborate with cross-functional teams to optimize latency and energy efficiency in high-frequency trading algorithms.
  • Conduct rigorous code reviews and mentor junior engineers in advanced algorithm development.
  • Stay at the forefront of industry trends in AI, quantum mechanics, and cybersecurity to ensure our systems remain future-proof.

Qualifications

  • Master’s or PhD in Computer Science, Physics, or a related quantitative field from an accredited institution.
  • Minimum of 7 years of experience in software engineering, with 3+ years specifically in Machine Learning and Quantum Computing.
  • Deep proficiency in Python, C++, and CUDA programming.
  • Proven track record of leading technical teams and delivering large-scale systems under tight deadlines.
  • Strong understanding of linear algebra, probability theory, and stochastic processes.

Required Skills

Python C++ Quantum Computing Machine Learning Neural Networks System Architecture CUDA Leadership Deep Learning

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