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Scientific Machine Learning Jobs (NOW HIRING)

Visible Hands

Machine Learning Engineer

Manhattan, NY · On-site

$150 - $190/hr

D. in Computer Science, Mathematics, EE, Physics, or a related quantitative field with a focus on Scientific Machine Learning (SciML). * Deep Learning Frameworks: 4+ years of expert-level experience ...

Root Access, Inc.

Machine Learning Engineer

Manhattan, NY · On-site

$140 - $190/hr

D. in Computer Science, Mathematics, EE, Physics, or a related quantitative field with a focus on Scientific Machine Learning (SciML). * Deep Learning Frameworks: 4+ years of expert-level experience ...

Root Access Inc

Machine Learning Engineer

New York, NY · On-site

D. in Computer Science, Mathematics, EE, Physics, or a related quantitative field with a focus on Scientific Machine Learning (SciML). * Deep Learning Frameworks: 4+ years of expert-level experience ...

Tacit

Machine Learning Scientist

San Francisco, CA · On-site

$180 - $270/hr

As a Machine Learning Scientist, you will develop cutting‑edge AI models to integrate and decode complex, multimodal data streams from our custom sensing hardware. You'll play a pivotal role in ...

Tacit

Machine Learning Scientist

San Francisco, CA · On-site

$180K - $270K/yr

About the role As a Machine Learning Scientist , you will develop cutting-edge AI models to integrate and decode complex, multimodal data streams from our custom sensing hardware. You'll play a ...

Apple

Machine Learning FEA Engineer

San Francisco, CA · On-site

S. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline along with 3+ years of relevant experience Preferred Qualifications Strong expertise in GNNs, CNNs, and ...

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Scientific Machine Learning information

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How much do scientific machine learning jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for scientific machine learning in the United States is $31.48, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $40.14 per hour, depending on experience, location, and employer.

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

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What states have the most Scientific Machine Learning jobs?

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Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $65,473 per year, or $31.5 per hour.

Quantum Solutions Engineer - Scientific Machine Learning M/W

Pasqal

Chicago, IL • On-site

Full-time

Posted 22 days ago

Job description

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.
Department Software Role Quantum Application Locations Chicago Remote status Hybrid Employment type Full-time Seniority Senior

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