Job Description
Shape the future of technology at QuantumLeap Technologies, where we're pioneering quantum computing breakthroughs that will redefine industries by 2026. We're seeking a visionary Quantum Computing Research Engineer to join our elite R&D team in San Francisco. As a key innovator, you'll develop next-gen quantum algorithms, optimize hardware-software interfaces, and collaborate with Nobel laureates on projects that push the boundaries of computational physics. Our state-of-the-art lab offers unparalleled resources, including access to quantum annealers and superconducting qubits. We offer competitive equity, flexible remote options, and a culture where your ideas could transform tomorrow's reality.
Responsibilities
- Design and implement novel quantum algorithms for optimization, cryptography, and machine learning applications
- Develop error-correction protocols to enhance quantum system stability and scalability
- Collaborate with hardware teams to optimize quantum software for specific qubit architectures
- Lead research initiatives in quantum machine learning and hybrid quantum-classical systems
- Publish findings in top-tier journals and present at international quantum computing conferences
- Mentor junior researchers and foster cross-functional innovation across physics, CS, and engineering teams
- Secure external funding through NSF and DoD quantum research grants
Qualifications
- PhD in Quantum Physics, Computer Science, or Computational Physics with 3+ years industry experience
- Expertise in quantum programming frameworks (Qiskit, Cirq, or Q#) and assembly-level quantum control
- Proven track record of developing quantum algorithms with measurable performance improvements
- Deep understanding of quantum error correction, fault tolerance, and decoherence mitigation
- Strong publication record in Nature/Science or equivalent quantum computing journals
- Experience with high-performance computing clusters and quantum hardware integration
- Exceptional problem-solving skills for abstract quantum system challenges