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Scientific Machine Learning Jobs (NOW HIRING)

TGS ASA

$140 - $190/hr

Lead the design, implementation, and evaluation of scientific machine learning models for subsurface and energy‑related data. * Contribute to the development of large‑scale representation ...

Calendly

Machine Learning Engineer

You will report to the head of Data Science & Machine Learning and will be responsible for building and operating ML-powered features that create magical experiences for our customers. Our team:

TGS ASA

Lead Data Scientist

Houston, TX · On-site

$140 - $190/hr

Lead the design, implementation, and evaluation of scientific machine learning models for subsurface and energy‑related data. * Contribute to the development of large‑scale representation ...

Socket.dev

Machine Learning Engineer

Los Angeles, CA · On-site

$180 - $250/hr

Role Description Founding Data Scientist / Machine Learning Engineer We're looking for a highly ambitious Data Scientist to help build the predictive intelligence layer behind nowfluence. This is not ...

Avride

Machine Learning Engineer

Austin, TX · On-site

Preferred : • Advanced degree in Computer Science, Machine Learning, Robotics, or a related field. • Experience developing ML algorithms for autonomous vehicles or robotics applications. • ...

Apple

Machine Learning FEA Engineer

Bodega Bay, CA

$142K - $263K/yr

D. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline Publications in top journals or conferences Minimum Qualifications Strong Expertise in Machine Learning, Deep ...

Apple

Machine Learning FEA Engineer

Bodega Bay, CA

$142K - $263K/yr

D. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline Publications in top journals or conferences Minimum Qualifications Strong Expertise in Machine Learning, Deep ...

Steg.AI

Machine Learning Scientist

Irvine, CA · On-site

$140 - $200/hr

Our machine learning team currently consists of 3 PhDs in Computer Vision.We are looking for a highly motivated machine learning scientist with a passion for groundbreaking AI technology for ...

Orchard LLC

Machine Learning Engineer

Machine Learning Engineer Remote with occasional travel to Silver Spring, MD About @Orchard ... Working at the intersection of computer science, marine science, and data analytics, this position ...

UNAVAILABLE

Machine Learning Engineer

Frisco, TX · On-site

$140 - $190/hr

D. preferred) in Computer Science, Machine Learning, or a closely related field. * Extensive knowledge of computer vision architectures such as Vision Transformers and VLMs along with OpenCV and PIL.

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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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Cities with the most Scientific Machine Learning job openings:

What states have the most Scientific Machine Learning jobs?

States with the most job openings for Scientific Machine Learning jobs include:

What job categories do people searching Scientific Machine Learning jobs look for?

The top searched job categories for Scientific Machine Learning jobs are:

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.

Lead Data Scientist

TGS ASA

On-site

$140 - $190/hr

Other

This job post has expired today. Applications are no longer accepted.

Job description

TGS provides scientific data and intelligence to the global energy sector, enabling energy for all by unlocking vital, data‑driven solutions and knowledge. Through an extensive and diverse energy data library, advanced analytics, cloud‑based applications, and specialized services, we work in a way that is Passionate, Results‑Driven, Collaborative, and Responsible.

Purpose & Scope

The Lead Data Scientist serves as a senior technical contributor within TGS’s Data Science organization, providing strong expertise in scientific machine learning and advanced analytics for complex subsurface problems. This role combines hands‑on model development with technical leadership across major initiatives, supporting the development of reusable learning systems for subsurface data, including foundation‑model–style representation learning. The position emphasizes scientific rigor, technical influence, and cross‑team collaboration, contributing to the design and evolution of large‑scale learning systems while working alongside other senior technical leaders.

Key Responsibilities
  • Lead the design, implementation, and evaluation of scientific machine learning models for subsurface and energy‑related data.
  • Contribute to the development of large‑scale representation learning systems, including self‑supervised and weakly supervised approaches.
  • Provide technical guidance and review for complex modeling initiatives, ensuring robustness, generalization, and reproducibility.
  • Own major technical workstreams and deliver scalable analytical solutions from research through deployment.
  • Collaborate closely with senior data scientists, domain experts, and engineering teams to align technical solutions with business and scientific objectives.
  • Guide experimentation practices, model evaluation standards, and technical documentation.
  • Mentor data scientists and support knowledge sharing across the organization.
  • Participate in external research activities, publications, or technical collaborations.
  • Scientific Machine Learning Expertise: Strong understanding of ML applied to physical or scientific systems.
  • Large‑Scale Representation Learning: Experience with modern deep learning architectures and training workflows for complex datasets.
  • Technical Leadership: Ability to guide technical workstreams and influence outcomes through expertise.
  • Experimental Rigor: Strong focus on hypothesis‑driven development and reproducible experimentation.
  • Collaborative Influence: Works effectively within multi‑lead, interdisciplinary environments.
  • Mentorship: Supports development of technical talent and best practices.
Qualifications
  • MSc or PhD in Machine Learning, Data Science, Applied Mathematics, Physics, Geophysics, or a related technical discipline.
  • 5–10 years of experience in applied data science or research‑oriented machine learning roles.
  • Strong background in modern deep learning and scientific ML applied to complex or large‑scale datasets.
  • Experience leading technical initiatives or complex modeling projects.
  • Experience in energy, geoscience, or large‑scale scientific/industrial domains preferred.

TGS is an Equal Opportunity Employer. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other protected status under federal, state, or local law.

We are committed to providing reasonable accommodations in our application process for individuals with disabilities. If you require an accommodation during the application or interview process, please contact us at hr@tgs.com.

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