Q
Machine Learning Engineer
В офисеПолная занятостьAI & Machine LearningQuantum ComputingElectronics Manufacturing
Design, deploy, and maintain machine learning systems that improve the calibration, control, and operation of quantum processors. You will develop reinforcement learning, Bayesian inference, probabilistic modeling, and agentic control solutions for noisy, non-stationary, safety-critical environments
Обязанности
- Develop reinforcement learning, Bayesian inference, and probabilistic modeling methods for parameter tuning, drift tracking, and adaptive measurement on real hardware.
- Develop real-time parameter steering for calibration during quantum error correction and between circuits.
- Build and maintain agentic frameworks for autonomous system control and calibration.
- Develop and maintain Python-based ML services and libraries integrated with quantum control software and hardware.
- Deploy, validate, and iterate on ML solutions with customers and partner laboratories in experimental environments.
- Collaborate with product, R&D, and hardware teams on libraries, SDKs, and training materials.
Требования
- Master’s or PhD in machine learning, physics, applied physics, quantum information science, or a related field.
- At least 4 years of relevant experience.
- Strong machine learning and deep learning experience, including hands-on experience with deep learning, reinforcement learning, or agentic AI.
- Strong Python proficiency for scientific or systems-oriented codebases.
- Solid software engineering fundamentals, including architecture, Git workflows, testing, and code review.
- Experience taking machine learning systems from prototype to deployment under real-world constraints such as non-stationary data, expensive evaluations, or safety-critical action spaces.
- Proven software development experience and strong technical communication skills.
- Ability to work independently and collaboratively across multidisciplinary teams.
- Customer-focused approach and strong problem-solving skills.
Будет плюсом
- Familiarity with quantum computing concepts, including qubit calibration, randomized benchmarking, quantum error correction, and optimal control.
- Experience with sim-to-real systems, multi-objective reinforcement learning, or meta-learning.
Условия и преимущества
- Work with diverse qubit types and quantum architectures.
- Connect ML models directly to real hardware in production laboratories.
- Collaborate with product, R&D, hardware, customers, and partner laboratories.
- Contribute to internal libraries, customer-facing SDKs, and training materials.