Job Description
We are at the forefront of the technological revolution, preparing for the monumental leap into 2026 and beyond. NexaCore Systems is seeking a visionary Senior Quantum-Enhanced Machine Learning Engineer to architect the next generation of hybrid computational models. If you are passionate about bridging the gap between classical algorithms and quantum supremacy, we want to meet you.
In this pivotal role, you will lead the research and development of quantum-classical hybrid algorithms designed to solve complex optimization problems in logistics, finance, and healthcare. You will work directly with our Chief Technology Officer and a world-class team of physicists and software engineers to build scalable solutions ready for deployment on early-access quantum hardware.
Why Join Us?
- Work on cutting-edge technology that defines the 2026 tech landscape.
- Competitive compensation package and equity options.
- Flexible remote-first culture with state-of-the-art Seattle HQ.
Responsibilities
- Architect Hybrid Models: Design and implement quantum-classical hybrid algorithms (e.g., VQE, QAOA) using Python and C++.
- Hardware Integration: Optimize code for execution on leading quantum processors (IBM, Rigetti, or D-Wave) and cloud-based quantum simulators.
- Roadmap Leadership: Define the technical roadmap for our ML infrastructure, ensuring scalability and fault tolerance for 2026 deployment.
- Data Pipeline Management: Build robust data pipelines to prepare classical data for quantum encoding and post-processing.
- Cross-Functional Collaboration: Partner with domain experts to translate business requirements into quantum computing solutions.
- Mentorship: Guide junior engineers and researchers, fostering a culture of innovation and continuous learning.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Physics, Mathematics, or a related field with a focus on Quantum Computing or Machine Learning.
- Experience: 5+ years of professional experience in Machine Learning, with a specific focus on quantum computing or advanced numerical methods.
- Technical Skills: Proficiency in Python (PyTorch, TensorFlow, NumPy) and experience with quantum software stacks (Qiskit, Cirq, or Pennylane).
- Algorithm Design: Strong background in linear algebra, probability theory, and optimization theory.
- Communication: Ability to articulate complex technical concepts to both technical and non-technical stakeholders.
- Problem Solving: Demonstrated ability to troubleshoot high-performance computing issues and optimize resource allocation.