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Scientific Machine Learning Jobs in Whittier, CA

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 ...

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 ...

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 ...

Overview We're looking for a talented and intensely curious Machine Learning Scientist with deep expertise in building and deploying production machine learning models, particularly reinforcement ...

For more information about Spotter, please visit Overview We're looking for a talented and intensely curious Machine Learning Scientist with deep expertise in building and deploying production ...

Required : • Bachelors in Computer Science, Electrical Engineering, Mechanical Engineering (or ... Machine Learning, including ownership of projects throughout the entire ML Lifecycle • ...

Required : • Bachelor's or Master's degree in Computer Science, Machine Learning, Robotics, or a related technical field. • 2+ years of hands-on industry experience developing and deploying ...

Machine Learning Engineer

Torrance, CA · On-site

$160K - $300K/yr

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling ... Bachelors in Computer Science, Electrical Engineering, Mechanical Engineering (or similar ...

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Scientific Machine Learning information

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How much do scientific machine learning jobs pay per hour?

As of Sep 6, 2026, the average hourly pay for scientific machine learning in Whittier, CA is $33.28, according to ZipRecruiter salary data. Most workers in this role earn between $20.34 and $42.45 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 cities near Whittier, CA are hiring for Scientific Machine Learning jobs?

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

Data Scientist Machine Learning

Rule14

Santa Monica, CA • On-site

$75K - $100K/yr

Full-time

Medical

Re-posted 21 hours ago


Job description

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 projects. You will be responsible for the end-to-end development and deployment of machine learning models, collaborating closely with cross-functional teams to deliver impactful solutions.

Responsibilities

  • Develop and train machine learning models for various applications
  • Perform feature engineering to enhance model performance
  • Select appropriate algorithms based on project requirements
  • Tune and optimize model performance for deployment
  • Clean and preprocess data to ensure model accuracy
  • Design experiments to validate and improve model outcomes
  • Collaborate within a small team to integrate machine learning solutions
  • Manage end-to-end deployment of machine learning models

Preferred Qualifications

  • Proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn
  • Experience with SQL for data querying and manipulation
  • Strong skills in statistical analysis and data visualization
  • Critical thinking and effective communication abilities

Company Description

Rule14 is an AI company and technology incubator focused on applying new approaches to uncover patterns and extract valuable intelligence within big data.

Whether helping businesses gain insight on customer loss prevention, designing targeted revenue enhancement campaigns, or uncovering fraud, waste and abuse within government programs, Rule14 empowers organizations assessing massive amounts of data for real-time decision-making.

Nventr.ai focuses on applying artificial intelligence to real-world operational environments where reliability, speed, and accuracy are critical. The company builds systems that integrate AI directly into day-to-day workflows, enabling better decision-making, automation, and coordination between software and physical operations.


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About Rule14

Sourced by ZipRecruiter

Industry

Software development

Company size

11 - 50 Employees

Headquarters location

Santa Monica, CA, US

Year founded

2011