Job Description
Welcome to the frontier of computing. We are seeking a visionary Senior Quantum AI Architect to lead our research into post-silicon computation and the future of intelligent systems. If you are passionate about the convergence of quantum mechanics and artificial intelligence, and want to shape the landscape of 2026 and beyond, we want to meet you.
As a key member of our R&D division, you will be responsible for designing and implementing algorithms that leverage the unique power of quantum processors to solve complex machine learning problems. You will work at the intersection of hardware and software, bridging the gap between theoretical physics and practical application.
Why Join Us?
We offer a competitive benefits package, including stock options, remote-first flexibility, and the opportunity to work on projects that define the next generation of technology.
Responsibilities
- Design and architect scalable quantum algorithms for machine learning and optimization problems.
- Collaborate with hardware engineers to optimize software performance for emerging quantum processors (NISQ era).
- Develop error mitigation and error correction strategies to ensure algorithmic stability.
- Train and fine-tune quantum neural networks using hybrid classical-quantum architectures.
- Lead research initiatives to explore novel applications in cryptography, drug discovery, and financial modeling.
- Mentor junior researchers and contribute to the technical roadmap of the engineering department.
- Present complex technical concepts to stakeholders and the broader scientific community.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Physics, Mathematics, or a related quantitative field.
- Minimum of 5+ years of experience in software engineering, with a strong focus on AI/ML.
- Deep understanding of quantum computing fundamentals (superposition, entanglement, qubits) and quantum algorithms (QAOA, Grover’s, VQE).
- Proficiency in Python and C++, with experience in quantum computing frameworks such as Qiskit, Cirq, or PyQuil.
- Strong background in linear algebra, probability theory, and optimization techniques.
- Experience with hybrid quantum-classical machine learning models (e.g., quantum support vector machines).
- Exceptional problem-solving skills and the ability to work in a fast-paced, research-driven environment.