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Senior System Software Engineer – Embedded AI Inference

NVIDIA -
München

Jetzt bewerben

Details zur Stelle

Vollzeit

Qualifikationen

CI/CD
Doktorat
Informatik
Git
Master
C++
Machine Learning
Linux
Unit-Tests
Künstliche Intelligenz
Python

Vollständige Stellenbeschreibung

NVIDIA is synonymous with innovation, boasting trailblazers who are shaping the world with their forward-thinking approaches. This is your chance to be part of a vibrant community that's redefining the technological landscape. Ready to shape the future of automotive technology with NVIDIA? Apply now to be part of a team that's revolutionizing the industry and driving innovation to new heights. Your potential awaits!

We're hiring a Senior Software Engineer to develop production automotive software for AI inference and agent orchestration in C++. Join us on an exhilarating journey, where you'll build out the foundation for next-generation automotive software applications: in-car agentic AI and inference of cutting-edge AI models (LLM, VLM, VLA). You would have the opportunity to shape cutting-edge AI frameworks that enable unprecedented in-car AI experiences and provide a reliable backbone for a new generation of Autonomous Vehicles. If you're passionate about building robust, high-performance AI systems that run on GPUs in real vehicles, we'd like to hear from you.

What you'll be doing:

Design, implement, and maintain C++ agentic AI and AI inference solutions for embedded production platforms.
Integrate PyTorch Deep Learning models into C++ pipelines, and deploy them for real-time inference on NVIDIA GPUs.
Build and extend testable, modular libraries and components, including interfaces to models, sensor drivers, and vehicle control.
Profile, debug, and optimize C++ and CUDA code to meet strict latency and throughput targets.
Collaborate closely with ML researchers, systems engineers, and automotive partners to turn prototype algorithms into production-ready implementations.

What we need to see:

Hands-on experience building agentic AI frameworks and with LLM / VLM inference. Experience with LLM and VLM inference and related optimization techniques like speculative decoding, LoRA, MoE.
8+ years of professional software engineering experience, ideally in high-performance safety-critical software, automotive, robotics, or real-time systems.
Master's or PhD degree in Computer Science or Machine Learning.
Strong modern C++ (C++14/17 or later): templates, RAII, smart pointers, STL, and experience building large codebases.
Solid Python skills for tooling, training scripts, and glue code between data pipelines and C++ components.
Experience developing on Linux: build systems (CMake), debugging (gdb, sanitizers), profiling, and git-based workflows in a CI/CD environment.
Familiarity with GPU programming and optimization, ideally with TensorRT.

Ways to stand out from the crowd:

Experience with agentic AI, specifically agents based on edge-friendly models (2–7B), including context management, reliable tool calling, and MCP, as well as experience with agentic coding.
Direct experience with the NVIDIA DRIVE AGX platform.
Knowledge of AI model optimization and deployment: quantization (INT8, FP8, 4-bit).
Familiarity with high-performance LLM inference frameworks like TensorRT-LLM or ONNX Runtime.
Understanding of software quality practices for safety-critical systems (code review, unit testing, static analysis; automotive standards knowledge is a plus) as well as open-source contributions or published work in AI, robotics, or GPU computing.

Work on challenging, real-world in-car AI inference problems where your ML and C++ skills directly impact the cabin experience and vehicle's self-driving capabilities. Collaborate with a talented, multidisciplinary team of researchers, engineers, and automotive experts. Solve hard technical problems at the intersection of deep learning, real-time systems, and production software engineering.

If this opportunity aligns with your background and interests, please apply with your resume and a brief description of relevant automotive AI projects (links to GitHub, publications, or technical write-ups are welcome).

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