Computational Engineer; Wildfire and Meteorology

Company Overview

Metrea delivers effects-as-a-service to national security partners across five domains and more than a dozen mission areas—including airborne ISR, electronic warfare, secure communications, aerial refueling, special mission aviation, aerial firefighting, and advanced simulation.

Wherever we operate, we build vertically integrated full stacks of capability—designing, building, and operating turnkey solutions that let customers scale capacity while benefiting from continuous cycles of innovation.

With operators and engineers under one roof, we close the gap between lab and field—what we call connecting design with effect.

Metrea’s solutions are built for elegance: effective, efficient, and evolving. This approach enables our partners to do more with less and achieve outsized, asymmetric advantage against rapidly evolving threats.

Headquartered in Washington, DC, Metrea has facilities across the United States, the United Kingdom, Europe, and beyond.

The Digital and Synthetic Capability Unit (D&S) is committed to providing mission-driven information solutions that seamlessly bridge the digital and physical realms. Leveraging cutting-edge technologies and advanced platforms, we empower operational readiness and elevate situational awareness across diverse domains—including air, maritime, and space.

We are seeking a Computational Scientist with a strong background in wildfire and environmental modelling to join our Digital & Synthetic Capability Unit. This role focuses on developing, improving, and ope rationalising wildfire prediction models to support our firefighting training simulator and enterprise-level decision tools.

You will design and implement numerical and statistical modelling approaches for wildfire spread and coupled atmospheric processes, run simulations in an automated and scalable HPC environment, and apply downscaling or bias-correction techniques to improve local prediction fidelity. The role requires translating scientific models into robust, production-quality tools that can be integrated into training systems and operational workflows.

In addition, you will contribute to the development of high-fidelity scientific visualisation pipelines within Unreal Engine, ensuring that wildfire and meteorological simulation outputs are transformed into accurate, real-time representations for immersive training and scenario analysis.

The successful candidate will combine scientific depth with practical software engineering capability, enabling models to be executed reliably at scale and integrated into decision-making processes across the organisation.

• Create and improve custom solvers for wildfire modelling and weather simulations
• Explore hybrid physics–statistical modelling approaches to enhance wildfire spread prediction
• Develop scripts, tools, and frameworks to automate end-to-end simulation workflows
• Run HPC simulations, including job submission, monitoring, and resource optimization on cluster environments
• Profile, benchmark, and optimise simulation performance across CPU and GPU architectures
• Apply statistical downscaling methods to improve local area weather modelling capabilities
• Contribute to scientific visualisation pipelines, including preparation of wildfire and meteorological datasets for real-time rendering in Unreal Engine

• Strong background in scientific computing, with experience in C/C++ and Python preferred
• Proficiency in scripting languages for workflow automation
• Hands‑on experience with HPC environments (job schedulers, cluster management, parallelisation)
• A strong background in fire modelling
• Experience implementing downscaling, upscaling, or bias-correction methodologies for environmental datasets
• Understanding of numerical methods, optimisation and simulation techniques
• Ability to translate research prototypes into maintainable, production-quality scientific software
• Experience working with cloud platforms, preferably Microsoft Azure for compute, data or workflow deployment
• Ability to design experiments, evaluate model performance, and communicate technical findings clearly to both technical and non-technical…