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Scientific Machine Learning Jobs (NOW HIRING)

Nvidia

OR

Experience with development and application of machine learning techniques to solve real world scenarios in weather/climate * Experience with scientific visualization. Strong analytical skills with ...

AEG

Data Scientist, Machine Learning

Orlando, FL · On-site

The Data Scientist, Machine Learning will support Basketball Operations by developing and deploying machine learning models to inform decision making across player evaluation, game strategy, and ...

Chabez Tech

Machine Learning Engineer

Portland, OR

... computer science, Machine Learning, Electrical Engineering, or related field • 5+ years of ... experience in machine learning, data science, or AI engineering • Strong programming skills in ...

Chabez Tech

Machine Learning Engineer

Portland, OR · On-site

... computer science, Machine Learning, Electrical Engineering, or related field • 5+ years of ... experience in machine learning, data science, or AI engineering • Strong programming skills in ...

Chabez Tech

Machine Learning Engineer

Portland, OR

... science, Machine Learning, Electrical Engineering, or related field 5+ years of experience in ... machine learning, data science, or AI engineering Strong programming skills in Python (NumPy ...

Spotify

Machine Learning Engineer

... scientists, machine learning engineers, and data engineers • Experience in optimizing machine learning models for production use cases • Familiarity with creating model success metric dashboards ...

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site

$88.50K - $121K/yr

This role blends scientific machine learning (surrogate modeling) with sequential decision-making under uncertainty. A successful candidate will: Explore: you're open-minded about methods and will ...

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How much do scientific machine learning jobs pay per hour?

As of May 31, 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 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 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 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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Scientific Machine Learning jobs near you
Senior Scientific Machine Learning Engineer - Earth-2

Senior Scientific Machine Learning Engineer - Earth-2

Nvidia

Santa Clara, CA

Full-time

Posted 23 days ago

Job description

NVIDIA's deep learning and HPC platforms have made a huge impact in various fields and are broadly used across leading academic institutions, start-ups, and industry, including the world's largest Internet companies. We need passionate and creative people to help us build the AI frameworks underlying the NVIDIA Earth-2 platform: a comprehensive family of open models, libraries, and frameworks that democratize global access to professional-grade weather and climate AI.

What you'll be doing:

  • Work with some of the brightest minds in a premier AI company to develop leading machine learning frameworks, NVIDIA PhysicsNeMo and NVIDIA Earth2Studio, for our academic and industrial partners to build scientific ML technology and workflows for weather, climate, and earth system modeling.

  • Work with internal project teams to validate applications built using the framework on NVIDIA's products, and integrate new functionalities from internal or external projects into the platform

  • Stay up to date with the latest research and innovations in deep learning techniques, implement and experiment with new ideas to develop and enhance NVIDIA's Earth-2 technologies, with a focus on weather & climate AI

What we need to see:

  • BS or MS degree (PhD preferred) in computer science, mathematics, computational science/engineering, or related technical field or equivalent experience

  • 5+ yrs of relevant experience

  • Strong Python programming skills

  • Familiarity with containers, numeric libraries, modular software design

  • Deep knowledge of state-of-the-art DNN architectures and machine learning techniques and algorithms (graph networks, diffusion models, reinforcement learning etc.) with experience in developing or using major deep learning frameworks (PyTorch, Tensorflow, JAX etc.)

  • Experience with development and application of machine learning techniques to solve real world scenarios in weather/climate

  • Experience with scientific visualization. Strong analytical skills with bias for action

  • Good time-management and organization skills to thrive in a fast paced, dynamic environment

  • Solid written and oral communications skills. Good teamwork and interpersonal skills

Ways to stand out from the crowd:

  • Experience using multi-node systems with data-parallel and model-parallel programming, performance optimization. Experience with HPC programming models (OpenMPI, NCCL), and/or CUDA or GPU kernel programming

  • Experience with nonlinear simulation tools and techniques, usage of major simulation codes. Published papers in the field of AI in scientific computing, especially in weather & climate applications

  • Familiarity with common tooling in the Earth-2 ecosystem (xarray, zarr, regridding, weather & climate data stores, etc.)

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. If you're creative and autonomous, we want to hear from you! NVIDIA's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern deep learning - the next era of computing - with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as "the AI computing company." We're looking to grow our company and establish teams with the most thoughtful people in the world.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June 1, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.#deeplearning
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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