Unreal Gigs
Quantum Machine Learning Engineer (The Quantum AI Trailblazer)
Unreal Gigs, San Francisco, California, United States, 94199
Quantum Machine Learning Engineer (The Quantum AI Trailblazer)
Are you passionate about merging quantum computing and artificial intelligence to solve problems that classical systems can't? Do you excel at creating innovative machine learning algorithms designed for quantum systems, unlocking new possibilities in optimization, predictive modeling, and data analysis? If you're ready to pioneer quantum-enhanced AI,
our client
has the perfect opportunity for you. We're seeking a
Quantum Machine Learning Engineer
(aka The Quantum AI Trailblazer) to develop and implement cutting-edge quantum machine learning (QML) algorithms that redefine the future of AI. As a Quantum Machine Learning Engineer at
our client , you'll collaborate with quantum physicists, AI researchers, and software developers to build hybrid quantum-classical systems that harness the power of quantum computing for advanced machine learning applications. Your expertise in quantum programming, machine learning, and optimization will be essential in driving innovation and delivering impactful solutions. Key Responsibilities: Develop Quantum Machine Learning Algorithms: Design and implement QML algorithms for applications such as classification, clustering, reinforcement learning, and generative models. Integrate Quantum and Classical Systems: Build hybrid frameworks that seamlessly combine quantum computing with classical machine learning techniques to enhance performance and scalability. Optimize Quantum Circuits for QML Tasks: Create and refine quantum circuits to efficiently implement machine learning operations, considering hardware constraints like gate fidelity and qubit decoherence. Collaborate on Real-World Applications: Apply QML algorithms to solve practical challenges in industries like finance, healthcare, logistics, and materials science, demonstrating quantum advantage. Test and Benchmark Algorithms on Quantum Platforms: Evaluate QML models using simulators and real quantum hardware, such as IBM Quantum, Google Quantum AI, or Rigetti. Analyze performance and compare results with classical approaches. Stay Updated on Advances in QML and Quantum Hardware: Research emerging techniques in quantum machine learning and assess their potential impact on the field. Incorporate the latest tools and methodologies into your work. Collaborate on Open-Source Contributions: Develop and contribute to open-source libraries and frameworks that support QML research and development, advancing the broader quantum computing community. Required Skills: Expertise in Quantum Computing:
Proficiency with quantum programming languages such as Qiskit, Cirq, TensorFlow Quantum, or PennyLane, with a solid understanding of quantum mechanics and hardware constraints. Machine Learning Mastery:
Strong knowledge of machine learning concepts, frameworks, and algorithms, with experience in libraries like TensorFlow, PyTorch, or Scikit-learn. Programming Proficiency:
Advanced skills in Python and familiarity with C++ or other high-performance programming languages. Mathematical and Analytical Skills:
Deep understanding of linear algebra, probability theory, and optimization techniques, essential for QML development. Problem-Solving and Creativity:
Ability to translate complex problems into innovative quantum-enhanced machine learning solutions. Educational Requirements: Master's or Ph.D. in Computer Science, Machine Learning, Physics, Mathematics, or a related field.
Equivalent experience in quantum computing and AI may be considered. Research publications or projects in quantum machine learning or related fields are highly desirable. Experience Requirements: 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. Familiarity with quantum hardware platforms and their limitations, such as noise and qubit count constraints.
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Are you passionate about merging quantum computing and artificial intelligence to solve problems that classical systems can't? Do you excel at creating innovative machine learning algorithms designed for quantum systems, unlocking new possibilities in optimization, predictive modeling, and data analysis? If you're ready to pioneer quantum-enhanced AI,
our client
has the perfect opportunity for you. We're seeking a
Quantum Machine Learning Engineer
(aka The Quantum AI Trailblazer) to develop and implement cutting-edge quantum machine learning (QML) algorithms that redefine the future of AI. As a Quantum Machine Learning Engineer at
our client , you'll collaborate with quantum physicists, AI researchers, and software developers to build hybrid quantum-classical systems that harness the power of quantum computing for advanced machine learning applications. Your expertise in quantum programming, machine learning, and optimization will be essential in driving innovation and delivering impactful solutions. Key Responsibilities: Develop Quantum Machine Learning Algorithms: Design and implement QML algorithms for applications such as classification, clustering, reinforcement learning, and generative models. Integrate Quantum and Classical Systems: Build hybrid frameworks that seamlessly combine quantum computing with classical machine learning techniques to enhance performance and scalability. Optimize Quantum Circuits for QML Tasks: Create and refine quantum circuits to efficiently implement machine learning operations, considering hardware constraints like gate fidelity and qubit decoherence. Collaborate on Real-World Applications: Apply QML algorithms to solve practical challenges in industries like finance, healthcare, logistics, and materials science, demonstrating quantum advantage. Test and Benchmark Algorithms on Quantum Platforms: Evaluate QML models using simulators and real quantum hardware, such as IBM Quantum, Google Quantum AI, or Rigetti. Analyze performance and compare results with classical approaches. Stay Updated on Advances in QML and Quantum Hardware: Research emerging techniques in quantum machine learning and assess their potential impact on the field. Incorporate the latest tools and methodologies into your work. Collaborate on Open-Source Contributions: Develop and contribute to open-source libraries and frameworks that support QML research and development, advancing the broader quantum computing community. Required Skills: Expertise in Quantum Computing:
Proficiency with quantum programming languages such as Qiskit, Cirq, TensorFlow Quantum, or PennyLane, with a solid understanding of quantum mechanics and hardware constraints. Machine Learning Mastery:
Strong knowledge of machine learning concepts, frameworks, and algorithms, with experience in libraries like TensorFlow, PyTorch, or Scikit-learn. Programming Proficiency:
Advanced skills in Python and familiarity with C++ or other high-performance programming languages. Mathematical and Analytical Skills:
Deep understanding of linear algebra, probability theory, and optimization techniques, essential for QML development. Problem-Solving and Creativity:
Ability to translate complex problems into innovative quantum-enhanced machine learning solutions. Educational Requirements: Master's or Ph.D. in Computer Science, Machine Learning, Physics, Mathematics, or a related field.
Equivalent experience in quantum computing and AI may be considered. Research publications or projects in quantum machine learning or related fields are highly desirable. Experience Requirements: 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. Familiarity with quantum hardware platforms and their limitations, such as noise and qubit count constraints.
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