Original listing text, shown exactly as published by the company.
About the role
The Performance Optimization team is at the heart of our mission: using Machine Learning to dramatically improve the quality, speed, and reliability of the quantum chip lifecycle.
As a Senior Machine Learning Engineer, you will own the architectural blueprint and technical direction of key components in our ML stack. This is a high-autonomy role where you’ll set the standards for how we build, scale, and deploy.
Responsibilities
- Architecture & Deployment: Lead the design and implementation of complex ML workflows. You will architect solutions that are not just accurate, but scalable, maintainable, and observable in production.
- Cross-Functional Leadership: Proactively collaborate with quantum physicists to identify bottlenecks in the chip lifecycle. You will translate high-level physical constraints into precise algorithmic requirements and drive the solution to completion.
- Technical Roadmap: Identify gaps in the ML stack and proactively propose, scope, and prioritize multi-quarter improvements. You will own the roadmap for one or more core optimization components.
- Mentorship & Standards: Conduct code reviews and guide junior engineers in software design patterns. You will play a key role in defining the "gold standard" for engineering within the optimization team.
- Engineering Influence: Contribute to engineering standards beyond the immediate team. Participate in cross-team design reviews, define reusable patterns, and help shape how ML is built across the organization.
Requirements
- 5+ years of industry experience in Machine Learning Engineering or Software Engineering with a strong ML focus, or a PhD + 3 years in the industry.
- Ownership Track Record: Demonstrated experience independently owning an ML project end-to-end, from ambiguous problem definition through production deployment and iteration.
- Technical Proficiency: Advanced expertise in Python and the modern ML stack (PyTorch/JAX). Proven experience building and maintaining production-grade software (not just notebooks).
- System Knowledge: Experience with MLOps tools, distributed training, or cloud infrastructure.
- Mathematical Fluency: Strong grasp of linear algebra and optimization, with the ability to discuss technical trade-offs with research scientists.
Nice to Have
- Deep Tech Experience: Experience working in hardware-constrained environments (robotics, semiconductors, physics) or with scientific computing.
- Research Impact: Lead authorship at top-tier ML conferences, or a background in Physics/Quantum mechanics.
Recruitment Process
Screening Call with Grace, Talent Acquisition Specialist (30 min)
Hiring Manager Interview with Etienne (45 min)
Technical Interview with the Team (90 min)
Leadership Team Interview (30 min)…
