Senior Quantum Solutions Engineer - ML

About Pasqal Pasqal designs and develops Quantum Processing Units (QPUs) and associated software tools. Our innovative technology enables us to address use cases that are currently beyond the reach of the most powerful supercomputers; these cases can concern industrial application challenges as well as fundamental science needs. In addition to the exceptional computing power they provide, QPUs are highly energy efficient and will contribute to a significant reduction in the carbon footprint of the HPC industry. Job Description We are looking for a candidate to join our Quantum Applications department delivery team to build and deliver client-facing solutions based on Pasqal’s quantum ML algorithm portfolio. his 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. As a Senior machine learning engineer with around 5 years of professional experience at least with interest in quantum computing (physics background is a plus, not a requirement), you will: • Adapt and implement Pasqal’s 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 Pasqal QPUs, taking into account 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. • Keeping a regular scientific and technological watch. As a Senior machine learning engineer with around 5 years of professional experience at least with interest in quantum computing (physics background is a plus, not a requirement), you will: • Adapt and implement Pasqal’s 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 Pasqal QPUs, taking into account 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. • Keeping 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 go to conferences/meetups Recruitment process • An interview with our talent acquisition of 30' • A meeting with the team and technical test of 60' • An exchange with Shaheen Engineering manager • A final interview with Louis-Paul, the VP • An offer ! PASQAL is an equal opportunity employer. We are committed to creating a diverse and inclusive workplace, as inclusion and diversity are essential to achieving our mission. We encourage applications from all qualified candidates, regardless of gender, ethnicity, age, religion or sexual orientation. Joining Pasqal will give you the opportunity to take an active part in the rapid development of a DeepTech scale-up at the forefront of the second quantum revolution. You will be directly involved in one of the biggest challenges shaping the technological landscape of the 21st century. You will be working as part of a team of top-level scientists and engineers, in direct competition with the world's biggest players. Show more Show less

Around 5 years of professional experience in ML or related field; interest in quantum computing; physics background is a plus but not a requirement.

Adapt and implement Pasqal’s 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 Pasqal QPUs, taking into account 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. Keeping a regular scientific and technological watch.