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Scientific Machine Learning Jobs in California (NOW HIRING)

Microsoft Corporation

Machine Learning

Mountain View, CA

$220K - $331K/yr

We aim to deliver breakthroughs that benefit society-advancing science, education, and global well ... Doctorate in Computer Science, Machine Learning, Human-Centered AI or related field AND 2+ year(s ...

New

Microsoft

Machine Learning

Mountain View, CA · On-site

$220K - $331K/yr

We aim to deliver breakthroughs that benefit society-advancing science, education, and global well ... Doctorate in Computer Science, Machine Learning, Human-Centered AI or related field AND 2+ year(s ...

Rule14

Be Seen First

Data Scientist Machine Learning

Santa Monica, CA · On-site

$75K - $100K/yr

As a Data Scientist Machine Learning, you will work within a small data science team focusing on predictive modeling, natural language processing, computer vision, recommender systems, and OCR ...

True Anomaly

Machine Learning Engineer

Long Beach, CA · On-site

Required : • Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a similar discipline • Proficient in Python • Foundational understanding of ...

Apple

Machine Learning FEA Engineer

Bodega Bay, CA

$139K - $258K/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

$139K - $258K/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 ...

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Scientific Machine Learning information

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 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 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 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.

What are popular job titles related to Scientific Machine Learning jobs in California? For Scientific Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in California look for? The top searched job categories for Scientific Machine Learning jobs in California are:
What cities in California are hiring for Scientific Machine Learning jobs? Cities in California with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in California as of June 2026, with employment types broken down into 78% Full Time, and 22% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.
Senior Scientific Machine Learning Engineer - Earth-2

Senior Scientific Machine Learning Engineer - Earth-2

Nvidia

Santa Clara, CA

Full-time

Posted 3 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 13, 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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