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Machine Learning Cfd Jobs (NOW HIRING)

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

... simulation engine, applying machine learning and AI to improve manufacturing processes ... CFD tools, meshing software, and CAD platforms for multiphysics simulations; contribute to ...

Neural Concept

ML for CAX Application Engineer

New York, NY · On-site

$125K - $200K/yr

You have an excellent understanding of both Engineering and Machine Learning (strict requirement) * You have experience with simulation. Industry experience in CAD / CAE (CFD, FEM, etc) modelling is ...

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How much do machine learning cfd jobs pay per year?

As of Jun 2, 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 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.

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 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.

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.

More about Machine Learning Cfd jobs
What cities are hiring for Machine Learning Cfd jobs? Cities with the most Machine Learning Cfd job openings:
What states have the most Machine Learning Cfd jobs? States with the most job openings for Machine Learning Cfd jobs include:
What job categories do people searching Machine Learning Cfd jobs look for? The top searched job categories for Machine Learning Cfd jobs are:
Machine Learning Cfd jobs near you
Infographic showing various Machine Learning Cfd job openings in the United States 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, with an average salary of $93,015 per year, or $44.7 per hour.
ML Engineer, Surrogate Modeling (Vehicle Engineering)

ML Engineer, Surrogate Modeling (Vehicle Engineering)

SpaceX

Hawthorne, CA • On-site

$145K - $175K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 12 days ago

SpaceX rating

8.7

Company rating: 8.7 out of 10

Based on 142 frontline employees who took The Breakroom Quiz

13th of 59 rated aerospace companies

Job description

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.
ML ENGINEER, SURROGATE MODELING (VEHICLE ENGINEERING)
Be a member of the AI for Vehicle Engineering team, focusing on developing high-performance surrogate models to solve complex physics and engineering problems for our launch vehicles and spacecraft.
Our team builds AI systems that accelerate engineering analysis, simulation, development, testing, avionics design, flight data review, logistics, and mission operations. Your work will directly support the world's largest communication and AI satellite constellations, accelerate rapid reuse of the Falcon launch vehicle, and contribute to the development of the world's largest rocket capable of sending humans to Mars.
In this role, you will develop, train, tune, and deploy AI surrogate models to dramatically accelerate engineering simulations, including but not limited to FEA, CFD, thermal, and structural analysis. You will work closely with hardware, simulation, and domain engineers to build these systems from the ground up. You will leverage state-of-the-art surrogate modeling techniques and create novel methodologies that push the frontier of what is possible in ML for physics while tackling real-world problems.
Aerospace experience is not required. We are looking for smart, motivated, collaborative engineers who love applying machine learning to hard scientific problems and want to make a direct impact on SpaceX's mission.
RESPONSIBILITIES:
  • Develop, train, evaluate, and deploy production-grade AI surrogate models that accelerate critical engineering simulation workflows
  • Design and implement State-of-the-Art (SOTA) neural architectures and training strategies tailored to complex engineering problem domains
  • Build scalable data pipelines to preprocess, manage, and utilize tens of thousands of high-fidelity simulation results
  • Stay current with the latest research in neural operators, physics-informed ML, and surrogate modeling, implementing new techniques when needed
  • Collaborate with peers on architecture, design, and code reviews
  • Deep dive into engineering problems to identify where AI can deliver the highest leverage and most reliable solutions
  • Develop and apply techniques for uncertainty quantification, active learning, and inverse problems (e.g., geometry and shape optimization)
  • Ensure all AI systems are rigorously validated and vetted for accuracy, robustness, and reliability in engineering use

BASIC QUALIFICATIONS:
  • Bachelor's degree in computer science, data science, engineering, math, physics, or a related technical discipline; OR 4+ years of professional experience building software in lieu of a degree
  • 1+ years of software development experience in Python for machine learning, AI, or data science applications

PREFERRED SKILLS:
  • Master's or PhD in computer science, machine learning, engineering, or a related field with a focus on surrogate modeling or AI for scientific/engineering simulation
  • Demonstrated experience training, tuning, and deploying production-grade ML surrogate models in real engineering workflows
  • Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural networks, or other surrogate model architecture
  • Experience solving inverse problems such as geometry optimization or design under uncertainty
  • Strong understanding of traditional simulation and numerical methods (CFD, FEA, thermal analysis, etc) and how to integrate them with surrogate models
  • Experience with uncertainty quantification techniques for surrogate models
  • Hands-on experience building active learning or adaptive sampling pipelines
  • Proficiency with deep learning frameworks such as PyTorch, TensorFlow, or JAX
  • Experience with surrogate modeling libraries such as NVIDIA PhysicsNemo or similar
  • Experience developing on Linux systems with GPU accelerators
  • Strong understanding of software engineering best practices including version control, testing, and continuous integration
  • Solid foundation in statistics, numerical methods, and core machine learning algorithms

ADDITIONAL REQUIREMENTS:
  • Ability to work extended hours and weekends as necessary

COMPENSATION AND BENEFITS:
Pay Range:
AI Software Engineer/Level I: $125,000.00 - $145,000.00/per year
AI Software Engineer/Level II: $145,000.00 - $175,000.00/per year
Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience.
Base salary is just one part of your total rewards package at SpaceX. You may also be eligible for long-term incentives, in the form of company stock, stock options, or long-term cash awards, as well as potential discretionary bonuses and the ability to purchase additional stock at a discount through an Employee Stock Purchase Plan. You will also receive access to comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short and long-term disability insurance, life insurance, paid parental leave, and various other discounts and perks. You may also accrue 3 weeks of paid vacation and will be eligible for 10 or more paid holidays per year. Employees accrue paid sick leave pursuant to Company policy which satisfies or exceeds the accrual, carryover, and use requirements of the law.
ITAR REQUIREMENTS:
  • To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.

SpaceX is an Equal Opportunity Employer; employment with SpaceX is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.
Applicants wishing to view a copy of SpaceX's Affirmative Action Plan for veterans and individuals with disabilities, or applicants requiring reasonable accommodation to the application/interview process should reach out to EEOCompliance@spacex.com.

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