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

FieldAI

2.53 3D Machine Learning Engineer

Irvine, CA · On-site

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 ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

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 ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

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 ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

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 ...

Yum! Brands

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. * 3+ years of experience in Machine Learning Engineering, MLOps, Software Engineering, or related ...

Yum Brands

Sr Machine Learning Engineer

Irvine, CA

$112K - $154K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. * 3+ years of experience in Machine Learning Engineering, MLOps, Software Engineering, or related ...

Yum

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

This role partners closely with Data Scientists, Data Engineers, and Analytics stakeholders to ... Help productionize machine learning models and data pipelines that support customer analytics ...

Crowe LLP

Machine Learning Intern

Costa Mesa, CA

$27 - $42/hr

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately overlapping the academic summer. DS interns will have a designated data scientist mentor and will ...

New

Crowe LLP

Machine Learning Intern

Costa Mesa, CA

$27 - $42/hr

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately overlapping the academic summer. DS interns will have a designated data scientist mentor and will ...

New

Turion Space

Machine Learning Engineer

Irvine, CA · On-site

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

As a Machine Learning Integration Engineer, you will help rapidly prototype, mature, and monitor ML ... A degree in Computer Science or a related field. Preferred Skills and Experience: * Experience ...

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All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in Irvine, CA

Scientific Machine Learning information

See Irvine, CA salary details

$14

$33

$56

How much do scientific machine learning jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for scientific machine learning in Irvine, CA is $33.79, according to ZipRecruiter salary data. Most workers in this role earn between $20.62 and $43.08 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 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 Irvine, CA?

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

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

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

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

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

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Irvine, CA as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 22% Part Time, 1% Temporary, and 2% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $70,278 per year, or $33.8 per hour.

2.53 3D Machine Learning Engineer

FieldAI

Irvine, CA • On-site

Full-time

Re-posted 6 days ago

Job description

Job Summary:
FieldAI is a company based in Irvine, California, specializing in embodied AI and robotics. They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D machine learning models for processing reality capture data, contributing to automated progress tracking and scene understanding in construction environments.
Responsibilities:
• Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
• Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
• Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
• Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
• Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.
Qualifications:
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 systems for 3D point clouds, perception, or spatial understanding tasks.
• Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
• Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
• Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
• Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
• Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
• Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.
Preferred:
• Experience working with BIM data, digital twins, or construction-related sensor data.
• Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.
• Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
• Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
• Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
• Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
• Experience building custom modules for SparseConvNet or 3D transformers.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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