The Role
As a Quantum Algorithms Engineer, you will design, prototype, and implement the "brains" of our platform. You will focus on the cutting edge of Quantum Optimization and Quantum Machine Learning (QML), ensuring that classical GPU accelerators and quantum hardware work in perfect, low-latency harmony.
Responsibilities
- Algorithm Design: Implement quantum and hybrid algorithms, focusing specifically on QAOA, VQE, and QUBO frameworks.
- Platform Integration: Collaborate with our engineering team to integrate gate-model routines into the Super Platform architecture.
- Performance Engineering: Optimize the interplay between GPU and QPU layers to achieve maximum efficiency and low-latency co-processing.
- R&D: Develop advanced gate-model routines and QML applications to solve complex industry problems.
- Technical Communication: Translate complex algorithmic concepts into actionable insights for both technical teams and stakeholders.
Required Qualifications
- Experience: 3–5 years in quantum computing or a related high-performance computing field.
- Technical Stack: Proficiency in Python and quantum SDKs like Qiskit, Cirq, PennyLane, or TKET.
- Cloud Infrastructure: Hands-on experience with cloud quantum services like AWS Braket.
- Mathematical Depth: Strong foundation in linear algebra, graph theory, and optimization (Ising-models, MILP, Parameterized Quantum Circuits).
- Hybrid Workflows: Demonstrated experience managing workloads across heterogeneous compute environments.
Preferred Skills (Bonus)
- Experience with NVIDIA CUDA-Q for GPU-Quantum co-processing.
- Direct experience working with physical quantum hardware.
- Active open-source contributions or peer-reviewed publications in QML or Optimization.
You can also send your CV to: mosadmin@superq.co or ejoy@superq.co
