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Machine Learning Cfd Jobs in San Jose, CA (NOW HIRING)

The Mice Groups, Inc.

Mechanical Engineer

Fremont, CA · On-site

This role focuses on applying computational engineering, machine learning, and digital-twin ... Conduct detailed CFD/FEA analyses including heat transfer, fluid and gas flow dynamics, stress ...

New

Meta

Thermal Design Engineer

Sunnyvale, CA

$118K/yr

Develop part and sub-system thermal and fan models using CFD and CAD tools * Develop and optimize ... Please note that Meta may leverage artificial intelligence and machine learning technologies in ...

UniversalAGI

Technical Product Manager

San Francisco, CA · On-site

$196K - $227K/yr

Bridge AI and Engineering: Act as the translation layer between our elite machine learning ... Experience with physical engineering, computational fluid dynamics (CFD), finite element analysis ...

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All JobsMachine Learning Cfd JobsMachine Learning Cfd Jobs in San Jose, CA

Machine Learning Cfd information

See San Jose, CA salary details

$12.9K

$109K

$154.7K

How much do machine learning cfd jobs pay per year?

As of Aug 28, 2026, the average yearly pay for machine learning cfd in San Jose, CA is $109,012.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,200.00 and $128,900.00 per year, depending on experience, location, and employer.

What is a machine learning CFD?

Machine Learning CFD (Computational Fluid Dynamics) jobs focus on integrating machine learning techniques with traditional fluid dynamics simulations and analyses. Professionals in this field use AI and data-driven models to accelerate simulations, improve prediction accuracy, and optimize fluid flow processes. These roles often require knowledge of both CFD principles and machine learning algorithms, and are commonly found in industries such as aerospace, automotive, and energy. Typical responsibilities include developing surrogate models for simulations, automating data analysis, and implementing deep learning approaches for complex flow problems.

How does a machine learning CFD professional typically collaborate with domain experts and software engineers in a project setting?

As a Machine Learning CFD (Computational Fluid Dynamics) professional, you’ll frequently collaborate with domain experts such as mechanical or aerospace engineers to ensure your models accurately reflect physical phenomena. You’ll also work closely with software engineers to integrate machine learning algorithms into simulation pipelines and optimize computational performance. Effective communication is key, as you’ll need to translate complex data-driven insights into actionable engineering solutions and vice versa. These collaborative efforts help streamline workflows, improve model accuracy, and ensure practical deployment of ML-enhanced CFD tools.

What are the key skills and qualifications needed to thrive as a machine learning CFD engineer, and why are they important?

To thrive as a Machine Learning CFD Engineer, you need a strong background in fluid dynamics, numerical methods, and machine learning, often supported by a degree in engineering, physics, or computer science. Familiarity with CFD software (such as ANSYS Fluent or OpenFOAM), programming languages like Python or C++, and machine learning frameworks (TensorFlow or PyTorch) is essential. Critical thinking, problem-solving, and effective communication are standout soft skills for interpreting data and collaborating on interdisciplinary teams. These competencies are crucial for developing innovative solutions that enhance simulation accuracy and computational efficiency in engineering projects.

What is the difference between Machine Learning CFD vs Data Scientist?

AspectMachine Learning CFDData Scientist
Required CredentialsDegree in Engineering, Computer Science, or related fields; knowledge of CFD softwareDegree in Statistics, Computer Science, or related fields; strong programming skills
Work EnvironmentEngineering firms, aerospace, automotive industries, research labsBusiness, finance, tech companies, research institutions
Industry UsageSimulation, fluid dynamics, engineering analysisData analysis, predictive modeling, business insights

Machine Learning CFD focuses on applying machine learning techniques to computational fluid dynamics simulations, often within engineering contexts. Data Scientists analyze large datasets to extract insights and build predictive models across various industries. While both roles require programming skills and a strong analytical background, Machine Learning CFD emphasizes simulation and engineering applications, whereas Data Scientists focus on data-driven decision-making across diverse sectors.

What are popular job titles related to Machine Learning Cfd jobs in San Jose, CA?

For Machine Learning Cfd jobs in San Jose, CA, the most frequently searched job titles are:

What job categories do people searching Machine Learning Cfd jobs in San Jose, CA look for?

The top searched job categories for Machine Learning Cfd jobs in San Jose, CA are:

What cities near San Jose, CA are hiring for Machine Learning Cfd jobs?

Cities near San Jose, CA with the most Machine Learning Cfd job openings:

Machine Learning Cfd jobs near you

Mechanical Engineer

Fremont, CA • On-site

The Mice Groups, Inc.
Human Resources Consulting Services • 11 - 50 employees
Learn more about The Mice Groups, Inc.external

Other

Posted yesterday

New

Job description

We are seeking a Mechanical Engineer to support advanced simulation, modeling, and design optimization for semiconductor equipment and mechanical systems. This role focuses on applying computational engineering, machine learning, and digital-twin approaches to accelerate design validation and development.


Job Description:

Support thermal, flow, and structural simulations for design validation and optimization of semiconductor equipment, subsystems, and components. Conduct detailed CFD/FEA analyses including heat transfer, fluid and gas flow dynamics, stress, structural response, vibration, and manufacturability assessments. Develop surrogate models, reduced-order models, and machine learning-based prediction tools to accelerate simulation turnaround and design iteration.

Build digital-twin frameworks by integrating physics-based simulation models with test, operational, and sensor data where available. Create automated workflows for design of experiments, high-fidelity simulation data generation, model training, and simulation-driven optimization. Perform uncertainty quantification, sensitivity analysis, parameter calibration, and model validation against detailed simulation or experimental data. Prepare technical reports and presentations communicating simulation assumptions, model accuracy, validation results, and design recommendations. Collaborate with internal design, process, software, controls, data science, and manufacturing teams to develop scalable simulation solutions.


Required:

  • Highly proficient in CFD and FEA simulation tools such as ANSYS Fluent, ANSYS Mechanical, STAR-CCM+, Simcenter 3D, or equivalent tools.
  • Strong background in fluid mechanics, heat transfer, solid mechanics, numerical methods, and engineering model validation.
  • Experience developing surrogate models, reduced-order models, response surface models, or other simulation acceleration approaches.
  • Strong programming skills in Python, MATLAB, or similar scientific computing environments.
  • Experience with 3D design tools such as Siemens NX, ProE/Creo, SolidWorks, or equivalent CAD platforms.
  • Excellent written and verbal communication skills, including technical presentation capability.
  • M.S. or Ph.D. in Mechanical Engineering, Aerospace Engineering, Computational Engineering, Applied Mathematics, Data Science, or a related field.


Preferred Qualifications:

  • Experience developing digital twins for engineering systems, semiconductor equipment, or manufacturing environments.
  • Experience with physics-informed machine learning, Gaussian process regression, neural networks, optimization algorithms, or design-space exploration methods.
  • Familiarity with cloud computing, HPC execution, simulation workflow automation, custom scripting, and data pipeline development.
  • Experience with simulation optimization, UDF packages, and automated parametric simulation workflows.
  • Semiconductor industry experience is a plus.

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