Interested in this AI/ML Engineer role at PASQAL?
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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.*
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At PASQAL, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
PASQAL AI Hiring
PASQAL has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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