Job Description
Join Nexus Labs at the forefront of computational revolution! We're seeking a visionary Quantum Computing Research Scientist to architect the next generation of algorithms that will redefine industries by 2026. This role offers unparalleled access to our 128-qubit quantum processors and collaborative partnerships with NASA, IBM Research, and MIT. You'll pioneer breakthroughs in quantum error correction, machine learning acceleration, and cryptography while mentoring a multidisciplinary team of physicists and software engineers.
Our state-of-the-art facility in San Francisco's Tech District features cryogenic labs, real-time quantum simulators, and unlimited compute resources. We offer competitive equity packages, flexible work arrangements, and dedicated R&D budgets for experimental projects. Join us to shape humanity's computational future.
Responsibilities
- Design and implement novel quantum algorithms for optimization problems and machine learning applications
- Develop error correction protocols for fault-tolerant quantum computation using topological qubits
- Collaborate with hardware teams to calibrate quantum processors and mitigate decoherence effects
- Lead research publications in peer-reviewed journals and present findings at international conferences
- Mentor junior researchers and cross-functional teams in quantum programming languages (Qiskit, Cirq)
- Secure patents for proprietary quantum methodologies and intellectual property
- Partner with government agencies on post-quantum cryptography standards development
Qualifications
- PhD in Quantum Physics, Computer Science, or related field with 3+ years industry research experience
- Proven track record publishing in Nature/Science journals or IEEE Transactions
- Expertise in quantum circuit design and measurement-based quantum computing
- Proficiency with quantum simulators (Q#, Qiskit Terra) and HPC frameworks
- Deep understanding of quantum information theory and entanglement protocols
- Experience with cryogenic systems and quantum hardware interfaces
- Strong background in machine learning frameworks (TensorFlow Quantum, PennyLane)
- Ability to secure SBIR/STTR grants or equivalent funding mechanisms