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Machine Learning Cfd Jobs in California (NOW HIRING)

DLH

Mechanical Engineer II

San Diego, CA · On-site

$95K - $130K/yr

They also develop power-using machines such as refrigeration and air-conditioning equipment ... Create Computational Fluid Dynamics (CFD) models of thermal steady state and non-steady state fluid ...

Applied Materials

Mechanical Engineer

Santa Clara, CA · On-site

$124K - $171K/yr

We empower our team to push the boundaries of what is possible-while learning every day in a ... Experience with engineering analysis tools (e.g., ANSYS, CFD-ACE, COMSOL ) is a plus.

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Machine Learning Cfd information

What are Machine Learning CFD jobs?

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 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 the key skills and qualifications needed to thrive as a Machine Learning CFD (Computational Fluid Dynamics) 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 are popular job titles related to Machine Learning Cfd jobs in California? For Machine Learning Cfd jobs in California, the most frequently searched job titles are:
What job categories do people searching Machine Learning Cfd jobs in California look for? The top searched job categories for Machine Learning Cfd jobs in California are:
What cities in California are hiring for Machine Learning Cfd jobs? Cities in California with the most Machine Learning Cfd job openings:
Machine Learning Cfd jobs near you
Infographic showing various Machine Learning Cfd job openings in California as of May 2026, with employment types broken down into 85% Full Time, 11% Part Time, 2% Contract, and 2% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.
AI/ML Scientist Lead Engineer

AI/ML Scientist Lead Engineer

Luminary Cloud

San Mateo, CA • On-site

Full-time

Posted 5 days ago

Job description

Luminary helps engineering companies be more competitive by getting to market faster, creating new, better products, and reducing development risk. We do this with our Physics AI platform, the fastest and easiest way to build and deploy models to understand and instantly predict physical reality with precision. Customers span industries from automotive and aerospace, to leading sporting equipment providers, including Otto Aviation, Joby Aviation, Piper Aircraft and Trek Bikes. Luminary is a Series B company and is headquartered in San Mateo, California.
The Role
We're looking for a visionary Physics AI leader to drive our vision for Physics AI. This role is a player-coach who will lead the Physics AI team at Luminary, while contributing concrete ideas and product architecture to drive the delivery of Physics AI foundation models. The role is responsible for driving how Luminary changes customer engineering design workflows forever
Responsibilities
  • Develop Physics-AI Tooling: Architect and implement high-performance tools for physics-informed workflows, similar in scope and capability to NVIDIA Modulus/Physics-ML (formerly Physics-Nemo), ensuring the delivery of models built off of synthetic data
  • Foundation Model Research: Lead the development of large-scale foundation models for the physical sciences, inspired by the collaborative, cross-domain approach of initiatives like Polymathic AI.
  • Architectural Innovation: Design and optimize specialized neural architectures for multi-scale physical systems, e.g.AB-UPT and related operator learning methods.
  • Physics-Informed Machine Learning (PIML): Embed physical constraints (conservation laws, symmetries, and PDEs) directly into the loss functions and inductive biases of deep learning models to ensure physical consistency and data efficiency.
  • Scalable Engineering: Collaborate with software engineers to deploy these models at scale within the Luminary Cloud platform, enabling real-time or near-real-time simulation for complex CFD/FEA problems.
  • Leadership: Drive the deliverables of the physics AI team each quarter contributing to the larger Luminary platform

Qualifications
Required
  • Masters degree or higher in Computer Science, Mechanical Engineering, Aerospace Engineering, or related field
  • 5+ years of experience building production software or ML systems
  • Experience with Physics Nemo models such as Domino and GeoTransolver
  • Experience with Geometry processing, Meshing, and physics solvers a must
  • Familiarity with developing LLM-powered applications a plus
  • Strong proficiency in Python
  • Proficiency using coding agents such as Claude Code
  • Familiarity with Physics AI, CAE, or physics simulation domains a critical requirement
  • Experience with distributed ML applications a big plus

What we are not looking for
  • Not looking for a pure manager for this role
  • Not looking for someone who has no background in Physics

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