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Physics Ai Simulation Jobs (NOW HIRING)

Hammerhead AI

Simulation Engineer

Redwood City, CA · On-site

About Hammerhead We're unleashing AI with intelligent orchestration while addressing one of the ... Architect and build high-fidelity, physics-based simulations of data center components, including ...

Luminary Cloud

Geometry/Meshing Engineer

San Mateo, CA · On-site

Develop and productize novel computational geometry handling and meshing approaches for physics simulation (e.g., CFD, structural analysis, thermal simulation) and Physics AI workflows with a focus ...

Everstar Inc

Founding AI Engineer

New York, NY · On-site

Push the frontier on physics models, world models, and AI-accelerated simulations. High-leverage IC role with founding-level impact. The Mission Everstar builds the intelligence layer that makes ...

Skild AI

Physics Simulation Scientist

San Mateo, CA · On-site

Position Overview We are seeking a Physics Simulation Scientist to lead advancements in the simulation and physics-solving backbone behind Skild's robot foundation model training. You will ...

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Physics Ai Simulation information

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$11K

$67.6K

$121.5K

How much do physics ai simulation jobs pay per year?

As of Jul 9, 2026, the average yearly pay for physics ai simulation in the United States is $67,601.00, according to ZipRecruiter salary data. Most workers in this role earn between $44,000.00 and $79,500.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior engineers in specialized fields such as aerospace, petroleum, or software engineering can earn $500,000 or more annually, especially with experience, advanced skills, and in high-demand industries. Roles involving leadership, project management, or working in lucrative markets often contribute to such high compensation levels.

How do professionals in Physics AI Simulation typically collaborate with other teams to develop accurate models?

In Physics AI Simulation roles, collaboration is integral to creating reliable simulation models. Professionals often work closely with domain experts, such as physicists and engineers, to validate the scientific accuracy of their AI-driven simulations. They also partner with software developers to integrate simulation tools into broader platforms and may engage with data scientists to refine algorithms using experimental or real-world data. Regular interdisciplinary meetings and code reviews are common practices to ensure alignment and quality throughout the project lifecycle.

What are the key skills and qualifications needed to thrive as a Physics AI Simulation Specialist, and why are they important?

To thrive as a Physics AI Simulation Specialist, you need a solid background in physics, mathematics, and computer science, often supported by a relevant degree or advanced studies. Familiarity with simulation software such as MATLAB, Simulink, or Unity, and programming languages like Python or C++, is typically required, along with experience in machine learning frameworks. Strong problem-solving, analytical thinking, and effective collaboration skills help distinguish top performers in this field. These competencies ensure accurate modeling, innovative solutions, and effective teamwork in developing realistic and efficient AI-driven simulations.

Can a physicist become an AI engineer?

A physicist can become an AI engineer by acquiring skills in programming, machine learning, and data analysis, often through online courses or advanced degrees. Their strong analytical and mathematical background can be a valuable asset in developing AI models and algorithms.

Which 3 jobs will survive AI?

Physics AI Simulation professionals are likely to see continued demand in roles involving complex problem-solving, data analysis, and developing AI models that require deep domain expertise. Jobs that involve creativity, emotional intelligence, and tasks requiring human judgment, such as research scientists, AI ethics specialists, and technical educators, are also expected to persist. These roles benefit from specialized knowledge and skills that are difficult for AI to fully replicate.

What is a Physics AI Simulation job?

A Physics AI Simulation job involves creating, developing, and maintaining computer simulations that model physical systems using artificial intelligence techniques. Professionals in this field use their expertise in physics, mathematics, and programming to design simulations for research, engineering, gaming, or educational purposes. They often work with machine learning algorithms to improve the accuracy and efficiency of these simulations, enabling better predictions and deeper insights into complex phenomena. This role typically requires strong analytical skills and experience with simulation software and programming languages.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior AI researcher, machine learning director, or AI architect, often requiring advanced skills in programming, data analysis, and deep learning. These roles are usually found in leading tech companies or specialized research organizations and may involve managing large projects or teams. Compensation at this level reflects extensive experience, expertise, and leadership responsibilities in the AI field.

What is the difference between Physics Ai Simulation vs Data Scientist?

AspectPhysics Ai SimulationData Scientist
Required CredentialsPhysics or Computer Science degree, knowledge of AI and simulation toolsStatistics, Mathematics, Computer Science degree, programming skills
Work EnvironmentResearch labs, tech companies, simulation software developmentBusiness, tech firms, data analysis teams
Industry UsagePhysics research, AI-driven simulations, scientific modelingData analysis, predictive modeling, business insights

Physics Ai Simulation focuses on creating AI-driven models to simulate physical phenomena, often requiring physics and AI expertise. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles involve data and AI, Physics Ai Simulation emphasizes physical modeling and simulation, whereas Data Scientists focus on data analysis and interpretation.

More about Physics Ai Simulation jobs
What cities are hiring for Physics Ai Simulation jobs? Cities with the most Physics Ai Simulation job openings:
What states have the most Physics Ai Simulation jobs? States with the most job openings for Physics Ai Simulation jobs include:
What job categories do people searching Physics Ai Simulation jobs look for? The top searched job categories for Physics Ai Simulation jobs are:
Physics Ai Simulation jobs near you
Infographic showing various Physics Ai Simulation job openings in the United States as of July 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $67,601 per year, or $32.5 per hour.

Procedural Data Generation Engineer

Vinci AI

Palo Alto, CA • On-site

$170K - $230K/yr

Full-time

Posted 13 days ago

Job description

About Us
Vinci is building physics AI for hardware design. Our models deliver ultrafast, accurate thermal and mechanical simulation and our platform puts that capability in the hands of every hardware engineer, not just simulation specialists. The quality of our models is bounded by the quality and diversity of the data they're trained on, and that data doesn't exist in the wild. We generate it.
General Description
You will join the data generation team and build the systems that produce the synthetic geometries, materials, boundary conditions, and simulation configurations used to train and evaluate our physics models.
This is a deep technical role at the intersection of computational geometry, physics simulation, and machine learning. The core challenge: write programs that generate families of hardware-like geometries and simulation setups - diverse enough to cover the space our models will see in production, constrained enough that every sample is valid, physically plausible, and useful as training signal.
What you'll do
  • Design and build procedural generators for parametric, hardware-like geometry using programmatic CAD (e.g., CadQuery, OpenCascade, Build123d) or another tool of your choice.
  • Generate physically plausible simulation configurations - boundary conditions, material assignments, loading scenarios - that respect real engineering constraints.
  • Define and measure diversity and coverage of generated distributions, and close the loop between dataset composition and model performance.
Qualifications
  • Software engineering skills, especially in Python
  • Hands-on experience with programmatic/parametric geometry: scripted CAD, B-rep and mesh representations, SDFs, or procedural generation in graphics tools.
  • Solid geometry processing fundamentals: meshing, boolean operations, voxelization/rasterization, and their numerical pitfalls.
  • Comfort reasoning about physical validity - you don't need to be a simulation expert, but you should care whether a boundary condition makes sense.

Nice to have
  • Exposure to ML training pipelines and how dataset composition drives model behavior
  • Familiarity with LLM-driven code generation.
  • FEA or thermal engineering experience.
  • Familiarity with quality-diversity algorithms, open endedness, deep learning fundamentals.
  • Familiarity with the hardware or semiconductor design process.

We're hiring across levels from strong mid-level through staff.
Why Join
  • Your work is crucial to the company's success. Model quality traces straight back to the data generating systems you'll build.
  • Work alongside researchers and engineers spanning ML, numerical methods, and hardware engineering on the frontier of AI.
  • Fast-growing startup where you own problems end to end.

Apply