About the Role
We are looking for a candidate to join our Quantum Applications department delivery team to build and deliver client-facing solutions based on our quantum ML algorithm portfolio. This is an application-driven engineering role focused on adapting, integrating, and deploying existing approaches to real-world partner and client use cases. The goal is to turn existing methods into reliable client deliverables; contributions to internal method improvement are welcome when they directly support project outcomes.
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As a Senior Machine Learning Engineer with around 5 years of professional experience, with an interest in quantum computing (a physics background is a plus, but not a requirement), you will:
- Adapt and implement our existing quantum(-enhanced) graph ML algorithms to fit client datasets, constraints, and performance targets.
- Build and maintain robust ML pipelines (data ingestion, preprocessing, training, evaluation, post-processing), including integration with quantum execution workflows, emulation/simulation platforms, and internal tooling.
- Establish baselines, metrics, and acceptance criteria with stakeholders, and drive solution iterations until delivery requirements are met.
- Collaborate closely with internal R&D teams to transfer methods from research to application, clarify assumptions/limits, and select the best approach from the portfolio for each use case.
- Work with hardware and platform engineers to run solutions on our QPUs, considering hardware constraints, execution constraints, and reliability.
- Work closely with domain experts from high-profile enterprises and partners to deliver solutions on real-world business problems.
- Produce maintainable code, documentation, and handover material so delivered solutions can be reused and supported.
- Keep a regular scientific and technological watch.
What We Offer
- Type of contract: Permanent contract
- A dynamic and close-knit international team
- A key role in a growing start-up
- Free time to train and attend conferences/meetups
Recruitment Process
- An interview with our talent acquisition team (30 minutes)
- A meeting with the team and a technical test (60 minutes)
- An exchange with the Engineering Manager
- A final interview with the VP
- An offer!
