qubitsok.com
Cut Noise. Work Quantum.
Europe, France, Paris
•
Posted 46 days ago
🏢 Alice & Bob
Role Type
Role Focus
Seniority
Employer Type
This Machine Learning Engineer role focuses on applying ML techniques to significantly enhance the performance and reliability of quantum computing chips. The successful candidate will be responsible for designing, implementing, and maintaining scalable and observable ML workflows in production. A key aspect involves working closely with quantum physicists to translate complex research concepts into robust software specifications. Additionally, the role includes setting high standards for software engineering and mentoring team members within the optimization team.
Key Responsibilities
Lead the design and implementation of complex ML workflows, architecting solutions that are scalable, maintainable, and observable in production.
Proactively collaborate with quantum physicists to identify bottlenecks in the chip lifecycle and translate high-level physical constraints into precise algorithmic requirements.
Ensure high availability and robustness of the optimization stack by championing best practices in testing, CI/CD, and versioning for machine learning models.
Optimize training pipelines on GPU clusters by identifying inefficiencies in job scheduling or data loading and implementing systemic fixes to reduce experiment turnaround time.
Define the gold standard for engineering within the optimization team by conducting code reviews and guiding junior engineers in software design patterns.
Required Skills
Industry experience of 3+ years in ML Engineering or Software Engineering with a strong ML focus, or a PhD.
Advanced expertise in Python.
Advanced expertise in the modern ML stack (PyTorch/JAX).
Proven experience building and maintaining production-grade software.
Experience with MLOps tools, distributed training, or cloud infrastructure.
Strong grasp of linear algebra and optimization.
Nice-to-have Skills
Experience working in hardware-constrained environments (robotics, semiconductors, physics) or with scientific computing.
Demonstrable history of taking an ML project from a vague concept to successful deployment in a user-facing or critical path system.
Lead authorship at top-tier ML conferences or a background in Physics/Quantum mechanics.
Technology Tags
The role involves using Machine Learning to dramatically improve the quality, speed, and reliability of the quantum chip lifecycle.
The technology is based on superconducting qubits, specifically the Schrödinger cat qubit.
Advanced expertise in Python and experience in building and maintaining production-grade software is required.
The team is the Performance Optimization team, and strong grasp of optimization is a required skill.
The role requires optimizing training pipelines on GPU clusters, indicating work within an HPC environment.
The underlying Schrödinger cat qubit technology is noted for implementing quantum error correction autonomously.
The goal of the ML effort is to improve the quality and reliability of the chip lifecycle, requiring analysis of noise and performance benchmarking.