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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 .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.