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Scientific Machine Learning Jobs in Perris, CA (NOW HIRING)

Esri

Sr. Generative AI Software Developer

Redlands, CA · On-site

$123K - $202K/yr

Develop Python-based machine learning components that enhance how users assess, understand, and ... Bachelor's degree in computer science, mathematics, or STEM related field Recommended ...

Esri

Sr. Generative AI Software Developer

Redlands, CA · On-site

$123K - $202K/yr

Develop Python-based machine learning components that enhance how users assess, understand, and ... Bachelor's degree in computer science, mathematics, or STEM related field Recommended ...

Esri

GIS Solution Engineer - AEC

Redlands, CA · On-site

... machine learning concepts • Programming and scripting experience with languages such as Python and JavaScript • Master's degree in geography, computer science, or a related field • Experience ...

Monster Energy

Senior Director, AI Innovation

Corona, CA · On-site

$270K - $310K/yr

Prefer a Bachelor's Degree in the field of -- Business, Computer Science, Artificial Intelligence (AI), Machine Learning (ML), Data Science, or related fields. Master's or MBA preferred * More than ...

Esri

GIS Solution Engineer - AEC

Redlands, CA · On-site

... machine learning concepts * Programming and scripting experience with languages such as Python and JavaScript * Master's degree in geography, computer science, or a related field * Experience with ...

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How much do scientific machine learning jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for scientific machine learning in Perris, CA is $32.09, according to ZipRecruiter salary data. Most workers in this role earn between $19.62 and $40.91 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.

What are popular job titles related to Scientific Machine Learning jobs in Perris, CA?

For Scientific Machine Learning jobs in Perris, CA, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Perris, CA look for?

The top searched job categories for Scientific Machine Learning jobs in Perris, CA are:

What cities near Perris, CA are hiring for Scientific Machine Learning jobs?

Cities near Perris, CA with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you

Computer Vision Engineer

Applied Medical

Rancho Santa Margarita, CA • On-site

$120K - $142K/yr

Full-time

Re-posted 14 days ago

Applied Medical rating

8.0

Company rating: 8.0 out of 10

Based on 23 frontline employees who took The Breakroom Quiz

Job description

Job Summary:
Applied Medical is a new generation medical device company committed to innovation and excellence in healthcare. The Computer Vision Engineer will develop and optimize algorithms for image and video analysis, integrating scalable solutions into real-world products while enhancing model accuracy and system efficiency.
Responsibilities:
• Design, develop, and implement computer vision algorithms for object detection, segmentation, tracking, and feature extraction in image and video analysis
• Collaborate with cross-functional teams to deploy and embed computer vision capabilities into products, including the Simsei simulation program
• Optimize the accuracy, efficiency, and scalability of existing computer vision systems for real-world applications
• Research emerging computer vision technologies and methodologies, applying insights to improve model performance
• Test, validate, and deploy models, maintaining robustness and reliability in production environments
• Monitor and troubleshoot deployed systems, resolving performance issues and implementing enhancements over time
Qualifications:
Required:
• Bachelor's degree in computer science, computer engineering, electrical engineering, or a related engineering field with an emphasis on computer vision, or relevant experience
• Minimum of three years of relevant work experience, or equivalent education and experience demonstrating the skills below
• Strong analytical skills and a solid foundation in mathematics
• Deep understanding of and hands-on experience with image processing, computer vision, machine learning, and deep learning algorithms
• Strong development skills in C++ or Python
• Experience applying traditional computer vision and deep learning algorithms to problems such as object detection, segmentation, tracking, and feature extraction
• Experience with data science and computer vision libraries, including NumPy, Pandas, SciPy, Matplotlib, Scikit-learn, and OpenCV
• Experience with deep learning frameworks such as PyTorch, TensorFlow, or Keras
Preferred:
• Experience with medical image analysis or medical applications
• Experience with image streaming and real-time image analysis
• Experience with computer graphics and visualization
• Experience with database servers such as MySQL or SQL Server
• Experience with cloud services such as Amazon Web Services (AWS), Azure, or Google Cloud Platform (GCP)
Company:
As a new generation medical device company, Applied Medical is focused on meeting three fundamental healthcare needs: enhanced clinical outcomes, cost containment and unrestricted choice. Founded in 1987, the company is headquartered in Rancho Santa Margarita, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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