Job Description
We are seeking a visionary Quantum-Enhanced Machine Learning Engineer to join the forefront of technological innovation in 2026. At Horizon Dynamics, we are building the infrastructure for the next industrial revolution, bridging the gap between classical deep learning and the power of quantum computing.
In this role, you will not just use existing tools; you will help define the protocols and algorithms that will power the world's most complex computational challenges. If you are passionate about the future of AI and possess a deep understanding of both neural networks and quantum mechanics, we want to hear from you.
Why Join Us?
• Work on cutting-edge projects that will define the 2026 tech landscape.
• Competitive equity package and comprehensive benefits.
• Collaborative, high-performance culture focused on excellence.
Responsibilities
- Design and implement novel quantum algorithms for machine learning tasks, specifically focusing on variational quantum eigensolvers and quantum neural networks.
- Collaborate with theoretical physicists to optimize quantum hardware for AI inference and training.
- Integrate quantum computing modules into existing cloud-based ML pipelines using Python and C++.
- Conduct rigorous research to evaluate the scalability of quantum models against classical benchmarks.
- Mentor junior engineers and contribute to internal technical documentation and research papers.
- Stay ahead of the curve by monitoring advancements in quantum hardware and next-gen AI architectures.
Qualifications
- PhD or Master’s degree in Computer Science, Physics, Applied Mathematics, or a related field with a focus on quantum information science.
- Strong proficiency in Python (PyTorch, TensorFlow) and C++.
- Experience with quantum computing frameworks such as Qiskit, Cirq, or Pennylane.
- Deep understanding of classical machine learning theory, including deep reinforcement learning and generative models.
- Proven track record of publishing research or implementing complex algorithms from scratch.
- Ability to communicate complex technical concepts to both technical and non-technical stakeholders.