Description:

The Quantum Machine Learning Engineer will develop and implement cutting-edge quantum machine learning (QML) algorithms that redefine the future of AI.
This role involves collaborating with quantum physicists, AI researchers, and software developers to build hybrid quantum-classical systems.
Key responsibilities include designing and implementing QML algorithms for applications such as classification, clustering, reinforcement learning, and generative models.
The engineer will build hybrid frameworks that seamlessly combine quantum computing with classical machine learning techniques to enhance performance and scalability.
They will create and refine quantum circuits to efficiently implement machine learning operations, considering hardware constraints like gate fidelity and qubit decoherence.
The role requires applying QML algorithms to solve practical challenges in industries like finance, healthcare, logistics, and materials science.
The engineer will evaluate QML models using simulators and real quantum hardware, analyzing performance and comparing results with classical approaches.
Staying updated on advances in QML and quantum hardware is essential, as is incorporating the latest tools and methodologies into their work.
The engineer will also develop and contribute to open-source libraries and frameworks that support QML research and development.

Requirements:

Expertise in quantum computing is required, including proficiency with quantum programming languages such as Qiskit, Cirq, TensorFlow Quantum, or PennyLane, along with a solid understanding of quantum mechanics and hardware constraints.
Strong knowledge of machine learning concepts, frameworks, and algorithms is necessary, with experience in libraries like TensorFlow, PyTorch, or Scikit-learn.
Advanced programming skills in Python and familiarity with C++ or other high-performance programming languages are essential.
A deep understanding of linear algebra, probability theory, and optimization techniques is required for QML development.
The candidate must possess problem-solving and creativity skills to translate complex problems into innovative quantum-enhanced machine learning solutions.
A Master’s or Ph.D. in Computer Science, Machine Learning, Physics, Mathematics, or a related field is required, with equivalent experience in quantum computing and AI considered.
Research publications or projects in quantum machine learning or related fields are highly desirable.
The candidate should have 3+ years of experience in machine learning or quantum computing, with a proven track record of developing algorithms or applications in QML.
Experience with hybrid quantum-classical workflows and real-world data applications is necessary.
Familiarity with quantum hardware platforms and their limitations, such as noise and qubit count constraints, is required.

Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums are provided.
The position offers competitive vacation, sick leave, and 20 paid holidays per year.
Flexible work schedules and telecommuting options promote work-life balance.
Opportunities for training, certification reimbursement, and career advancement programs are available for professional development.
Access to wellness programs, including gym memberships, health screenings, and mental health resources, is provided.
Life insurance and short-term/long-term disability coverage are included.
Confidential counseling and support services for personal and professional challenges are available through the Employee Assistance Program (EAP).
Financial assistance for continuing education and professional development is offered through tuition reimbursement.
Opportunities to participate in community service and volunteer activities are encouraged.
Employee recognition programs celebrate achievements and milestones.