Job Description
Join Nexus Quantum Dynamics at the forefront of 2026's technological revolution. We're pioneering quantum machine learning applications that will redefine computing paradigms. As a Quantum Machine Learning Engineer, you'll architect hybrid quantum-classical systems that solve previously impossible problems in cryptography, drug discovery, and climate modeling. Our state-of-the-art lab in San Francisco's tech corridor offers unparalleled resources to push the boundaries of what's computationally feasible.
We're seeking visionaries who thrive at the intersection of quantum physics and AI. You'll collaborate with Nobel laureates and industry disruptors to develop novel algorithms that leverage quantum supremacy. This role offers equity, flexible work arrangements, and continuous learning opportunities in one of the world's most innovative environments.
Responsibilities
- Design and implement quantum machine learning algorithms leveraging IBM Quantum and D-Wave systems
- Develop hybrid quantum-classical neural networks for real-world applications
- Optimize quantum circuit performance for NISQ-era hardware limitations
- Create predictive models using quantum-enhanced data analysis techniques
- Collaborate with cross-functional teams to integrate quantum solutions into production systems
- Publish research in top-tier quantum computing journals and conferences
- Mentor junior engineers in quantum programming best practices
Qualifications
- PhD in Quantum Computing, Machine Learning, or Physics (or equivalent experience)
- Expertise in quantum programming languages (Qiskit, Cirq, Q#)
- Proven experience with tensor networks and quantum simulation frameworks
- Strong Python/C++ skills with high-performance computing optimization
- Published research in quantum machine learning or related fields
- Familiarity with quantum error correction and fault-tolerant architectures
- Experience with cloud quantum computing platforms (AWS Braket, Azure Quantum)