Job Description
Are you ready to shape the future of technology? FutureScale Dynamics is seeking a visionary Principal Quantum AI Architect to lead our R&D division in San Francisco. In this pivotal role, you will bridge the gap between classical machine learning and emerging quantum computing paradigms, preparing our infrastructure for the quantum era of 2026 and beyond. You will work on cutting-edge algorithms, optimize for Noisy Intermediate-Scale Quantum (NISQ) devices, and define the architectural standards for our next-gen AI systems. If you thrive on solving impossible problems and want to be at the forefront of the next industrial revolution, we want to meet you.
Why join us?
- Work on the bleeding edge of technology with a world-class engineering team.
- Competitive compensation package including equity.
- Flexible remote-first policy with a vibrant office in the heart of SF.
- Access to the latest quantum hardware and cloud resources.
Responsibilities
- Algorithm Development: Design and implement scalable quantum algorithms for complex AI problems, including optimization, machine learning, and cryptography.
- System Integration: Collaborate with cross-functional teams to seamlessly integrate quantum modules into existing classical cloud infrastructure.
- Research & Innovation: Conduct deep research on error mitigation, quantum error correction, and hybrid quantum-classical models.
- Strategic Leadership: Lead the technical strategy for the transition from classical to quantum computing architectures and mentor junior researchers.
- Performance Tuning: Optimize hardware-software co-design for future quantum processors to maximize computational efficiency.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Physics, Mathematics, or a related field with a focus on quantum mechanics or computational theory.
- Experience: 5+ years of experience in software engineering, with at least 2 years specifically focused on quantum computing or advanced AI.
- Technical Skills: Proficiency in quantum computing frameworks (Qiskit, Cirq, Pennylane) and programming languages (Python, C++, Rust).
- Theory: Deep understanding of linear algebra, probability, and classical ML algorithms and how they translate to quantum circuits.
- Communication: Ability to explain complex quantum concepts to non-technical stakeholders and present research findings at conferences.