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Propulsion Engineer
Harmattan AI —Lausanne, VD
Domain Expertise: Practical understanding of propulsion physics and experience with electric propulsion systems or UAV environments.

Je souhaite recevoir la dernière alerte Emploi pour les postes de ce type : python (vaud)

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Aerodynamics Methodology and Software Engineer

Harmattan AI
Lausanne, VD

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Détails de l'emploi

100%

Qualifications

CI/CD
Niveau Doctorat
MCP
Git
Niveau Master
Bash
Bases de données
AWS
C++
Niveau Licence
Scripting
Linux
GitLab
Python

Description complète du poste

About Us

Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.

Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.

Role

As an Aerodynamics Methodology & Software Engineer at Harmattan, you will bridge the gap between aerospace physics and high-performance software engineering. Your mission is to professionalize our internal computational ecosystem, manage our HPC/Cloud infrastructure, and transform research-grade scripts into production-ready engineering software. You will act as the lead architect for our agentic AI workflows, developing custom MCP servers and LLM-ready repositories to automate complex simulation loops and design optimisations. You are the engineer of the automated toolchains that allow our team to iterate on UAV designs with unprecedented speed and fidelity.

Responsibilities

Software Professionalization: Refactor research scripts and "specialist tools" into modular, high-performance, and maintainable Python/C++ libraries. Implement robust unit-testing and documentation standards. Make sure the team members follow the structure when developing code.
AI-Augmented Engineering & Orchestration: Architect agentic workflows and custom MCP (Model Context Protocol) servers to bridge LLMs with internal CFD solvers and databases; codify engineering tribal knowledge into SKILLS.md or CLAUDE.md files to enable AI-driven code refactoring, automated simulation setup, and intelligent data analysis.
MDAO Toolchain Integration: Architect a unified design environment by developing APIs and automated workflows that link disparate tools (e.g., OpenVSP, XFoil, and OpenFOAM) into seamless optimization loops.
Infrastructure & Scalability: Manage and optimize our calculation infrastructure, including Linux-based HPC clusters (Slurm) and/or Cloud computing (AWS/Azure).
Aero Database (ADB) Management: Design the data architecture for storing and retrieving high-dimensional aerodynamic results, ensuring the GNC and flight physics teams have "single-source-of-truth" access to vehicle performance data.

Requirements

Education: B.S., M.S., or Ph.D. in Aerospace Engineering, Computer Science, or Informatics with a proven track record in the aerospace sector.
Aerospace Background: Familiarity with the aerospace physics, data structures and I/O of industry tools such as OpenFOAM, ANSYS, XFOIL, OpenVSP, or QProp.
Advanced Programming: Expert proficiency in Python (NumPy, SciPy, Pandas) and ideally C++.
Modern AI & Agentic Systems: Proven ability to develop LLM-integrated tools and MCP servers that automate engineering tasks; experience maintaining "AI-ready" repositories using structured instruction files and building neural surrogate models to accelerate physical simulations.
DevOps & Infrastructure: Knowledge of Git/GitLab (CI/CD, Runners) and Linux/Unix environments. Experience with Bash scripting and Cloud-scale computing (AWS).

Nice-to-Have

Numerical Methods: Hands-on experience using CFD or other physical solvers. Able to select appropriate physics models (for example turbulence models), configuring numerical schemes, and fine-tuning solvers.
Machine Learning in Data Science: Knowledge in setting up surrogate models and tools for engineering practices (surrogate models)

We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.

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Aerodynamics Methodology and Software Engineer

Propulsion Engineer

Consumer Electronics Solution Owner

Principal/Staff Software Engineer

Tech Lead Vertical Solutions

Senior Design Engineer (ID: 2605)

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