About PASQAL
PASQAL builds neutral-atom quantum computers and the full software stack required to run impactful hybrid quantum-classical workloads. Our quantum software ecosystem spans open- and closed-source libraries, developer tooling, documentation, and production-grade interfaces used by internal teams and external users.
About the role
Pasqal's Quantum Graph Machine Learning (QGML) team is looking for a senior candidate to design and drive quantum-enhanced machine learning methods, from early research to deployment on real client use cases and Pasqal quantum hardware. You will combine a strong technical background (physics, mathematics, or computer science) with hands-on machine learning engineering experience. You will take ownership of technical directions, not just execute a spec.
Who we are
Within Pasqal, the Quantum Graph Machine Learning (QGML) team sits at the crossing of analog quantum computing and graph-based machine learning. We are a small team of five people with diverse backgrounds, spanning physics, computer science, machine learning, and applied mathematics. We focus mainly on internal research, sometimes in collaboration with external partners. Our role is to explore new quantum graph machine learning methods, test them seriously, and turn the strongest ideas into reusable algorithmic assets for Pasqal. When a research direction becomes mature enough, it feeds Pasqal's algorithm portfolio. These methods can then be taken further, industrialized, and executed by the delivery teams in client-facing projects.
Responsibilities
- Track advances in machine learning and quantum computing, and bring in ideas that matter for the team's roadmap
- Keep a regular technological watch.
- Contribute to the development of novel scientific and technical approaches related to the Company's research, products, technologies, and markets; file patents and publish research articles when relevant.
- Lead the design and implementation of quantum-enhanced machine learning models, on classical hardware and on Pasqal's neutral-atom QPU.
- Own the classical components of ML pipelines: data preprocessing, post-processing, and integration with quantum hardware or simulation platforms.
- Collaborate with hardware engineers to align algorithm design with real QPU constraints and co-design next generations of Pasqal QPUs.
- Collaborate with Quantum Solutions teams and partners to translate business problems into a rigorous technical approach.
- Mentor junior team members and review their technical work.
- Present results and innovations in technical discussions internally and in related conferences and outreach events.
What we expect
- 5+ years of relevant experience in applied research, quantum algorithms, or machine learning engineering (PhD experience counts).
- Strong taste for applied mathematics, graphs, and machine learning.
- Solid foundation in quantum physics, mathematics, or computer science.
- Track record of shipping algorithms from idea to working, tested code.
- Experience running and interpreting numerical simulations at scale.
- Proficient in Python. Another scientific programming language is a plus.
- Comfortable writing clear technical reports and documentation for both technical and non-technical audiences.
- Able to work with some autonomy on ambiguous problems and push back on weak methodology.
- English fluency, written and spoken.
Nice to have
- Notions of quantum computing, atomic physics, and optics.
- Experience working directly with external clients or partners.
Recruitment process
- Talent acquisition interview 30’
- Chat with Shaheen, Engineering manager
- Technical interview
- Meet the team at the office
- Offer!
Pasqal est un employeur garantissant l'égalité des chances. Nous nous engageons à créer un lieu de travail diversifié et inclusif, car l'inclusion et la diversité sont essentielles à la réalisation de notre mission. Nous encourageons les candidatures de tous les candidats qualifiés, quels que soient leur sexe, leur race, leur origine ethnique, leur âge, leur religion ou leur orientation sexuelle
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