PASQAL designs and develops Quantum Processing Units and dedicated software tools. These innovative processors address applications which are out of the reach of the most powerful existing supercomputers, encompassing real-world challenges as well as fundamental science. As they are very low energy intensive, they will significantly contribute to reduce the carbon footprint of the computing industry.
PASQAL has partnerships with key users in the fields of energy, IT, finance, drug and chemical design, automotive. The maturity and potential of our technology and the quality of our scientific team has been rewarded several times at French, European and global levels.
Description
We are looking for a Quantum Solutions Engineer to join our Quantum Applications department and build client-facing solutions based on PASQAL’s quantum algorithm portfolio.
This application-driven engineering role focuses on adapting, integrating, and validating quantum and quantum-enhanced machine learning methods for real-world partner and client use-cases, with a strong focus on molecular and chemical applications.
The goal is to turn PASQAL’s existing methods into reliable client deliverables by combining scientific machine learning, Graph Machine Learning, and analog quantum computing.
Contributions to internal method improvement are welcome when they directly support project outcomes.
You will join as a Scientific Machine Learning Engineer specializing in chemistry applications, working at the interface between graph machine learning, quantum algorithms, and industrial use cases.
With strong engineering skills and an interest in quantum computing (physics background is a plus), you will:
· Adapt and implement PASQAL’s existing quantum and quantum-enhanced Graph ML algorithms for client datasets, scientific constraints, and performance targets.
· Translate scientific and chemical use cases into well-defined machine learning tasks, such as molecular property prediction, classification, ranking, or candidate screening.
· Select and implement suitable representations for molecules and chemical systems, including physicochemical descriptors, fingerprints, molecular graphs, and quantum feature representations.
· Integrate ML pipelines with quantum execution workflows, emulation and simulation platforms, PASQAL QPUs, and internal tooling.
· Collaborate closely with internal R&D teams to transfer quantum methods from research to application, clarify their assumptions and limitations, and select the most appropriate approach from PASQAL’s portfolio.
· Work closely with chemistry experts from clients and partners to understand the scientific meaning, quality, and limitations of molecular and experimental data.
· Produce maintainable code, technical documentation, benchmark reports, and handover material so delivered solutions can be reproduced, reused, and supported.
· Maintain an active scientific and technological watch in Quantum Machine Learning, Graph Machine Learning, and molecular machine learning.
This list is non exhaustive.
About you
- Master’s degree or PhD in Machine Learning, Computational Chemistry or Quantum Physics
- 2+ years of experience in a similar role
- Strong ML engineering background, including model training and evaluation, classical baselines, metrics, and reproducible experimentation.
- Hands-on experience with graph-structured data and Graph Machine Learning, such as graph kernels, Graph Neural Networks, or graph representations.
- Familiarity with quantum computing or quantum mechanics concepts and constraints, including the differences between classical simulation, emulation, and hardware execution.
- Working knowledge of fundamental chemistry concepts and familiarity with molecular representations such as descriptors, fingerprints, molecular graphs, or SMILES.
- Strong interest in applying quantum computing to practical machine learning and scientific problems.
- Experience working with molecular, chemical, materials, or other scientific data.
- Ability to build end-to-end ML pipelines (pre/post-processing, integration with existing tools/platforms).
- Physics background (quantum/atomic/optics) is a plus.
- Delivery mindset and ownership (client-facing deliverables, pragmatism, trade-offs).
- Strong communication and collaboration with internal R&D, hardware, and platform teams.
Right to work in USA without sponsorship is preferred.
What we offer
- Flexible schedules to support work/life balance
- A dynamic, close-knit, collaborative, and diverse international team for co-workers
- An impactful role in a growing scale-up that is leading in the Neutral Atom Quantum Computing space
- Competitive benefit packages
- Lots of time off to enjoy the things you love outside of work
- Free time to learn and attend conferences/meetups
- Employment Terms : Full time, Direct hire, Hybrid
Recruitment process
- An interview with our talent acquisition team via Teams Video meeting
- A 1 hour video interview with hiring manager via Teams Video meeting
- For technical roles: A technical Interview round via Teams Video with the hiring manager
- Final Interview
- 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, music preference, ethnicity, age, religion or sexual orientation.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The emergence of Quantum Solutions Engineers specializing in Scientific Machine Learning represents a vital structural link within the deep-tech ecosystem, bridging fundamental algorithmic innovation and commercial application delivery. As analog and digital quantum processing hardware matures, enterprise organizations encounter significant translation barriers when integrating specialized graph machine learning workflows into production-grade pipelines for chemical and molecular discovery. This role type serves as a primary technical bridge, converting theoretical algorithmic advantage into reproducible, high-throughput software deliverables for heavy-industry partners. Sector-wide efforts continue to address talent and integration challenges in quantum systems, underscoring the structural necessity of roles that unify domain-specific scientific computing with hybrid quantum architectures. By establishing rigorous verification standards for scientific machine learning applications, this function mitigates adoption risk across high-value verticals. Consequently, these positions act as essential catalysts for elevating quantum-enhanced computational pipelines across the broader global value chain.
The application enablement layer of the quantum computing value chain is undergoing a critical transition from exploratory proof-of-concept testing to standardized domain-specific integration. While quantum processing hardware advances across neutral-atom, trapped-ion, and superconducting architectures, software execution paradigms remain fragmented. The primary bottleneck for industrial technology adoption lies in the software-hardware translation layer, where classical enterprise datasets must be transformed into quantum-native representations without incurring prohibitive computational overhead.
Current industry dynamics reflect an urgent requirement for specialized interface profiles that connect pure quantum algorithmic research with heavy-industry domain constraints, particularly in computational chemistry and materials science. Commercial deployment is frequently hindered by Technology Readiness Level (TRL) mismatches between raw quantum processing hardware capabilities and the operational robustness demanded by end-user organizations. Hybrid classical-quantum frameworks, leveraging graph neural networks and scientific machine learning, are emerging as the primary mechanism to mitigate these NISQ-era hardware limitations.
Furthermore, workforce scarcity at the intersection of quantum information science, graph machine learning, and domain chemistry restricts the execution velocity of deep-tech scale-ups. As public funding initiatives and enterprise R&D budgets prioritize practical quantum utility, the ability to build, validate, and support reproducible software workflows becomes a core determinant of commercial viability. Roles operating at this interface ensure that algorithmic performance gains translate directly into scalable computational assets.
The technical architecture for scientific quantum machine learning focuses on the integration of domain-specific data structures with hybrid computational execution platforms. Core technical domains encompass graph machine learning representations, molecular feature engineering, and quantum circuit feature mapping. These capabilities enable the seamless translation of physical and chemical constraints into algorithmically tractable models.
Interface layers rely on robust integration between classical high-performance computing pipelines, quantum hardware backends, and emulation environments. Mastery of classical baseline benchmarking, software containerization, and automated execution workflows ensures the reproducibility and stability of hybrid computational solutions. Operating at the confluence of algorithmic research, software engineering, and customer-facing delivery, Pasqal leverages these capabilities to accelerate technology deployment and maintain software reliability across diverse compute infrastructures. - Accelerates the transition of scientific machine learning research into industrial-grade enterprise deliverables
- Reduces integration friction between hybrid quantum computing algorithms and established high-performance computing workflows
- Standardizes molecular and graph data representations across specialized quantum execution backends
- Strengthens client adoption velocity through rigorous classical baseline benchmarking and model validation
- Mitigates software deployment risks by establishing maintainable, reproducible engineering standards
- Enhances cross-functional alignment between pure algorithmic research and hardware control platform development
- Optimizes computational resource allocation across classical emulators, simulators, and physical quantum processing units
- Expands enterprise quantum readiness within pharmaceutical, chemical, and advanced materials manufacturing sectors
- Enables scalable domain-specific feature mapping for complex graph-structured scientific datasets
- Improves technology transfer efficiency from early-stage exploratory research to production-level software modules
- Secures competitive differentiation for deep-tech scale-ups by building robust, domain-tailored algorithm portfolios
- Supports global industrial decarbonization goals through energy-efficient quantum-enhanced computational modelingIndustry Tags: Quantum Computing, Scientific Machine Learning, Graph Machine Learning, Neutral Atom Processing, Computational Chemistry, Hybrid Quantum-Classical, Deep Tech, Enterprise Software, Algorithmic Benchmarking
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