Machine Learning Engineer at Quantum Machines - Delft Netherlands (Customer Success).
Quantum Machines is a global leader in control systems for quantum computing, a field on the verge of exponential growth, bringing about opportunities like those made possible with the invention of classical computing 50 years ago. We are assembling the strongest team of professionals in the world with the goal of revolutionizing how quantum computers are built and controlled and accelerating their arrival. Quantum Machines is backed by top-tier investors such as Battery Ventures, TLV Partners, Red Dot Capital, and Avigdor Willenz’s investment group.
Apply for the Machine Learning Engineer role.
QUANTUM ROLE CONTEXT | Appended by Quantum.Jobs v2
Role context:
This role exists to apply statistical models and data-driven algorithms to technical workflows and customer success operations within hardware control systems environments. Situated between software development, control hardware engineering, and external technical clients, the position translates complex operational data into actionable algorithmic models. By building predictive pipelines and optimizing calibration protocols, professionals in this function assist end-users in achieving consistent system performance, bridging raw computational methodologies with real-world technical implementations across customer deployment environments.
Quantum ecosystem relevance:
The role supports the quantum ecosystem by integrating intelligent data processing methods into control hardware and user support operations. As quantum hardware platforms grow in complexity, machine learning models help automate device calibration, noise characterization, and optimal control sequence generation. By enabling client-facing teams to deploy data-driven troubleshooting tools, this function accelerates hardware adoption, reduces setup overhead for research laboratories, and enhances overall measurement fidelity across multi-qubit experimental platforms.
Capability signals:
- Experience in designing and deploying predictive machine learning models within technical engineering environments
- Demonstrated proficiency in analyzing complex hardware control datasets to optimize system performance and operations
- Strong background in developing automated algorithmic pipelines for real-time data analysis and system calibration
- Proven capability in collaborating with cross-functional technical teams and client-facing engineering organizations
- Expertise in applying data science frameworks to complex physical and hardware-oriented domain challenges
