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Senior Edge AI & Robotics Engineer; f-m-d

in 42103, Wuppertal, Nordrhein-Westfalen, Deutschland
Unternehmen: Aptiv PLC
Vollzeit position
Verfasst am 2026-08-29
Berufliche Spezialisierung:
  • Software Entwicklung
    Künstliche Intelligenz Ingenieur, Robotik, Eingebettete Systeme, Maschinelles Lernen
Gehalts-/Lohnspanne oder Branchenbenchmark: 90000 - 150000 EUR pro Jahr EUR 90000.00 150000.00 YEAR
Stellenbeschreibung
Stellenbezeichnung: Senior Edge AI & Robotics Engineer (f-m-d)

Senior Edge AI & Robotics Engineer About Aptiv

Aptiv is a global technology leader with more than 200,000 employees across 48 countries. We develop advanced software, compute platforms, and intelligent systems that enable autonomous driving, advanced driver-assistance systems (ADAS), connected mobility, and intelligent infrastructure. Aptiv technology is embedded in millions of vehicles worldwide and powers some of the industry's most advanced automotive platforms.

As part of our expansion into Robotics and Adjacent Markets, Aptiv is developing scalable Edge AI and intelligent computing platforms for autonomous mobile robots (AMRs), drones, industrial automation, and next-generation intelligent machines. These platforms combine edge AI, heterogeneous compute, embedded software, safety-critical systems, and cloud-connected architectures to deliver real-world autonomy and decision-making at the edge.

As a Senior Edge AI & Robotics Engineer, you will serve as a hands‑on technical leader focused on the deployment, optimization, and scaling of AI workloads on embedded and edge computing platforms. You would work at the intersection of AI, embedded systems, software architecture, and heterogeneous computing, enabling production‑ready AI solutions that operate under real‑world constraints for performance, latency, power, memory, reliability, and safety.

Experience with robotics, autonomy is highly valued but not required.

Key Responsibilities Edge AI Platform Development
  • Design, develop, and deploy AI/ML solutions on embedded and edge computing platforms.
  • Optimize inference performance across CPU, GPU, NPU, DSP, FPGA, and heterogeneous SoC architectures.
  • Build scalable Edge AI software pipelines supporting computer vision, sensor fusion, multimodal AI, and real‑time decision‑making applications.
  • Deploy and optimize models using frameworks such as TensorRT, ONNX Runtime, OpenVINO, TFLite, TVM, or equivalent technologies.
  • Drive model optimization through quantization, pruning, compression, distillation, and hardware acceleration techniques.
  • Develop reusable software frameworks and deployment workflows that can scale across multiple product lines and hardware platforms.
Embedded Systems & Software Engineering
  • Develop high‑performance software in C/C++ for embedded and edge AI applications.
  • Optimize performance, memory utilization, power consumption, startup time, and system throughput.
  • Design software architectures that enable portability across different hardware platforms and operating environments.
  • Work with Linux‑based embedded systems, RTOS environments, containerization technologies, and cross‑compilation tool chains.
  • Collaborate closely with hardware teams to influence compute architecture, accelerator selection, sensor integration, and platform trade‑offs.
Edge AI Validation & Production Readiness
  • Define benchmarking, profiling, and validation strategies for AI applications deployed at the edge.
  • Establish performance, reliability, robustness, and scalability metrics.
  • Support the transition from research prototypes to production‑grade software solutions.
  • Develop automated testing, integration, and deployment workflows and CI/CD pipelines.
  • Collaborate with safety, systems, and quality teams to support safety‑critical and mission‑critical deployments.
Robotics & Intelligent Systems (Preferred)
  • Contribute to Edge AI solutions deployed in robotics, autonomous systems, industrial automation, and intelligent machines.
  • Support integration with robotics middleware and autonomy stacks where applicable.
  • Collaborate with robotics teams on perception, localization, planning, and control powered by Edge AI.
Basic Qualifications
  • Master's degree or PhD in Computer Science, Electrical Engineering, Artificial Intelligence, Robotics, Computer Engineering, or a related discipline.
  • 5+ years of experience developing and deploying AI/ML workloads on embedded or edge computing platforms.
  • Experience with NVIDIA Jetson, Qualcomm, TI, NXP, Renesas, AMD, Intel, or similar embedded AI platforms.
  • Expert level proficiency in C/C++ and Python.
  • Hands‑on experience with AI model deployment and optimization on resource‑constrained systems.
  • Deep understanding…
Stellen-Anforderungen
10+ Jahre Berufserfahrung
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