PhysicsX · 3 hours ago
Simulation Engineering Intern - Fire CFD
New York, United States
Internship
Onsite
Intern
$45/hr - $55/hrPhysicsX is a deep-tech company focused on advancing hardware innovation through AI-driven simulation software. The Simulation Engineering Intern will develop automated CFD simulation workflows and generate high-fidelity datasets for machine learning applications in computational fire dynamics, contributing to the development of AI-driven fire prediction tools.
AI InfrastructureArtificial Intelligence (AI)Information TechnologyMachine LearningSemiconductorSimulationSoftware
Responsibilities
Develop programmatic geometry generation workflows for complex layouts with parametric variation in configurations, spatial arrangements, ventilation systems, and structural elements
Build automated simulation generation pipelines implementing design-of-experiments strategies to explore diverse fire scenarios including ignition locations, heat release rates, fire propagation patterns, and suppression system responses
Configure and manage large-scale simulation campaigns on cloud HPC infrastructure, including batch job submission, and monitoring workflows for parallel simulations
Implement automated post-processing routines to extract key fire safety metrics including temperatures, smoke characteristics, toxic gas concentrations, heat flux distributions, and time-based egress calculations
Collaborate with data scientists and machine learning engineers to structure simulation outputs for training datasets, understand data quality requirements, and participate in model validation workflows
Generate high-fidelity fire modeling datasets spanning diverse configurations to support ML surrogate model development, working closely with data scientists and machine learning engineers on model architecture, hyperparameter optimization, and validation strategies to ensure accurate AI-driven fire behavior predictions
Research and validate simulation methodologies by reviewing technical literature on fire modeling, documenting material properties, benchmark studies, and relevant fire safety codes and standards
Develop comprehensive technical documentation explaining automation pipelines, fire modeling approaches, underlying physics being simulated, and references to literature and industry standards
Contribute to potential publication of research findings in peer-reviewed journal paper or PhysicsX internal publication, documenting methodologies and insights from fire modeling dataset generation and AI-driven models
Qualification
Required
Currently pursuing a PhD (or Masters) degree in Mechanical Engineering, Civil Engineering, Aerospace Engineering, Fire Protection Engineering, or related engineering field
Strong experience with computational fluid dynamics software (CFD) and fire modeling tools
Proficiency in Python programming for automation, data processing, and workflow orchestration
Coursework or demonstrated knowledge in fire dynamics, heat transfer, fluid mechanics, and combustion
Experience with Linux/Unix operating systems and command-line scripting
Strong problem-solving skills and ability to work independently on technical challenges
Preferred
Prior experience with CFD, and fire simulations using Fire Dynamics Simulator (FDS), ANSYS Fluent, Star CCM+, and/or OpenFOAM, and experience with visualization tools such as SmokeView, ParaView, and Blender
Knowledge of fire behavior physics, thermal dynamics, and suppression strategies
Familiarity with fire safety codes (IBC, IFC), standards (NFPA, SFPE), tests and research studies (FM Global, NIST, RISE, etc.)
Experience applying data science and machine learning methods to real-world engineering applications, with a focus on driving measurable impact in industry settings
Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, PyTorch, MLFlow) (note: no prior ML experience required)
Experience with HPC job scheduling systems and cloud computing platforms
Background in parametric design automation, CAD scripting, or mesh generation
Company
PhysicsX
PhysicsX offers an AI-native simulation software stack for engineering and manufacturing across advanced industries.
Founded in 2019
51-200 employees
H1B Sponsorship
PhysicsX has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2024 (1)
Funding
Current Stage
Growth StageTotal Funding
$187MKey Investors
NVenturesAtomicoGeneral Catalyst
2025-11-19Series B· $20M
2025-06-22Series B· $135M
2023-11-27Series A· $32M
Leadership Team
Jacomo Corbo
CEO & Co-Founder
Recent News
Tech Funding News
2025-11-08
Company data provided by crunchbase