> Markdown version of [/jobs/ext/1165794-ml-runtime-engineer](https://www.wearedevelopers.com/jobs/ext/1165794-ml-runtime-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Runtime Engineer - **Company:** Loft Orbital Solutions - **Location:** Toulouse, France - **Contract:** Permanent contract - **Skills:** C++ (Programming Language), Continuous Integration, Debian Linux, Image Management, Python (Programming Language), Yocto, ONNX (Open Neural Network Exchange) Format, TensorRT - **Published:** July 3, 2026 - **Apply:** https://fr.indeed.com/viewjob?jk=887c1cdb3eaab668 ## About the Role * Strong C++ and Python * Model compilation: TensorRT (and/or equivalent graph compilers) * ONNX Runtime, quantization & inference perf optimization * Embedded / edge GPU deployment (NVIDIA Jetson) * Benchmarking & profiling / perf tooling * CI/CD incl. hardware-in-the-loop, * Writing custom kernels / operator plugins * Remote sensing / large-image handling * RF / IQ signal data exposure * Meson build, Yocto / minimal Debian images * Model-weight protection / secure execution ## Description * Own model compilation: turn partner ONNX models into optimized engines per accelerator (TensorRT, Hailo, AMD/ROCm), within power & thermal budgets * Build the ground-side compile & delivery path; define how a mismatched engine is detected/rejected on-node * Build performance tooling: benchmarking, profiling, operator-coverage matrices, and budget validation * Contribute to Space Inference Engine (execution providers) and the SDK (model build & packaging); coordinate the OBSW / Runtime team ## Related Videos - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Discover the open source trio you didn’t expect: .NET and PostgreSQL on Linux](https://www.wearedevelopers.com/videos/2042-discover-the-open-source-trio-you-didn-t-expect-net-and-postgresql-on-linux) - [Making neural networks portable with ONNX](https://www.wearedevelopers.com/videos/301-making-neural-networks-portable-with-onnx) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices](https://www.wearedevelopers.com/videos/1582-llmops-driven-fine-tuning-evaluation-and-inference-with-nvidia-nim-nemo-microservices) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Dev Digest 138 - Are you secure about this?](https://www.wearedevelopers.com/magazine/486-dev-digest-138-are-you-secure-about-this) - [A 5-Step Open-Source Setup for Agentic Engineering](https://www.wearedevelopers.com/magazine/738-a-5-step-open-source-setup-for-agentic-engineering) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)