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Machine Learning Cfd Jobs (NOW HIRING)

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

$145K - $175K/yr

Strong C++ software engineering skills and experience applying machine learning (ML) and AI to ... Develop and optimize next-generation software that integrates CFD tools, meshing software, and CAD ...

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

$145K - $175K/yr

Strong C++ software engineering skills and experience applying machine learning (ML) and AI to ... Develop and optimize next-generation software that integrates CFD tools, meshing software, and CAD ...

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

$145K - $175K/yr

Strong C++ software engineering skills and experience applying machine learning (ML) and AI to ... Develop and optimize next-generation software that integrates CFD tools, meshing software, and CAD ...

Envision Energy

Conceptual Blade Design Engineer

Boulder, CO · On-site

$115K - $150K/yr

... machine learning. * Understanding of fundamental aerodynamics as it relates to low Mach, steady and ... Perform 2D/3D blade and airfoil CFD using panel-methods and/or commercial CFD solvers * Performing ...

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Machine Learning Cfd information

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$93K

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How much do machine learning cfd jobs pay per year?

As of Aug 6, 2026, the average yearly pay for machine learning cfd in the United States is $93,015.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,500.00 and $110,000.00 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 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 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.
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Machine Learning Cfd jobs near you
Infographic showing various Machine Learning Cfd job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $93,015 per year, or $44.7 per hour.

Senior/Principal Forward Deployed Engineer - Applied AI/ML

Luminary Cloud

San Mateo, CA • On-site

$142K - $197K/yr

Full-time

Re-posted 20 hours ago

Job description

Full-time position | San Mateo, CA (Onsite)
JOIN THE REVOLUTION IN ENGINEERING INNOVATION
Luminary helps engineering companies be more competitive by getting to market faster, creating better products, and reducing development risk. We do this through our Physics AI platform - the fastest and easiest way to build and deploy models that understand and instantly predict physical reality with precision. Our customers span industries from automotive and aerospace to defense, industrial, semiconductors, and energy - ranging from hyper-growth startups to Fortune 100 enterprises. Luminary is a Series B company headquartered in San Mateo, California.
YOUR IMPACT
As a Senior/Principal Applied AI/ML Scientist on Luminary's Applied AI/ML team, you build the Physics AI models that power customer outcomes. You work in a matrix structure inside customer value delivery teams alongside a Lead Delivery Engineer, Applications Engineers, and Data & Platform engineers. You take real customer engineering problems, design and train the right model architectures, and partner with the team to deploy those models into production engineering workflows. You operate at the boundary of cutting-edge research and applied delivery - staying connected to the frontier of physics-informed ML while making sure your work ships and gets used.
WHAT YOU'LL DO
  • Own model development for Physics AI engagements: architecture selection, training pipeline design, hyperparameter tuning, evaluation, and validation against ground-truth simulation.
  • Work with Applications Engineers to ensure training data is physically meaningful and adequate for the target use case.
  • Partner with Data & Platform engineers to operationalize training pipelines, model registries, and inference serving.
  • Collaborate with Luminary Research to apply state-of-the-art techniques - neural operators, diffusion models, geometric deep learning, latent representations - to real customer problems.
  • Embrace co-engineering: work side-by-side with customer data scientists and engineers, sharing methodology and building model literacy on the customer side.
  • Bring back signal from delivery into Research and Product, helping shape the next generation of Luminary's Physics AI methods and platform.
  • Mentor junior team members and contribute to internal best practices for applied physics-informed ML.

WHAT YOU BRING
  • 5-10 years of experience in applied machine learning, with significant exposure to scientific computing, engineering simulation, or physics-informed ML. Principal-level candidates trend toward the upper end of the range.
  • Strong proficiency in Python and PyTorch (or equivalent deep learning framework). You write production-quality ML code, not just research notebooks.
  • Hands-on experience training and deploying models on engineering or scientific data - surrogate models, neural operators, graph neural networks, diffusion models, or related architectures.
  • Working knowledge of engineering simulation: CFD, FEA, EM, thermal, or related - enough to collaborate effectively with domain experts and understand what the model needs to learn.
  • Experience with distributed training, GPU workloads, and modern ML infrastructure (experiment tracking, model registries, inference serving).
  • Strong scientific mindset: rigorous experimentation, careful evaluation, honest reporting of what works and what does not.
  • Customer-facing presence; comfortable explaining model architectures and limitations to engineering audiences.
  • Self-starter mentality, persistent through iteration, willing to travel occasionally to customer sites.

PREFERRED QUALIFICATIONS
  • Advanced degree (MS or PhD) in Computer Science, Applied Math, Physics, Engineering, or related quantitative discipline.
  • Published work in physics-informed ML, neural operators, scientific machine learning, or related fields.
  • Experience with physics-informed AI/ML frameworks (e.g. PhysicsNeMo, JAX-based scientific ML stacks) or foundation model fine-tuning pipelines for scientific data.
  • Prior experience in applied research roles at engineering, simulation, or scientific computing companies.
  • Track record of shipping models into production engineering workflows.

Apply