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Open nowPosted 7 hours ago

Edge AI Engineer (Mensch)

Neura Robotics382 open roles

Where
Metzingen / Riederich
Work mode
On site
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Your applicationOpen nowEdge AI Engineer (Mensch)Neura Robotics · Metzingen / Riederich

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  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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The reply lands in your private mailbox

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The clock on this job

Early applications get read.

8.0% of postings close within 7 days. Measured by our own scanner across the market. Neura Robotics postings stay open a median of 5 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.6%3 days
  3. 8.0%7 days
  4. 15.0%14 days
  5. 34.2%30 days
This job: posted 7 hours ago

Neura Robotics median: 5 days open

The posting

DEINE MISSION & HERAUSFORDERUNGEN

Als Edge AI Engineer bringst du modernste Modelle als produktionsreife, on-device Intelligenz auf unsere Roboter. Du arbeitest hands-on an der Schnittstelle von Machine Learning, Embedded Systems und Robotik. Dein Fokus: Modelle schnell, schlank und zuverlässig auf der Hardware zum Laufen zu bringen, die wir ausliefern.

- Deployment am Edge: Du bringst Modelle vom Training bis in den Einsatz und nutzt dafür Quantization, Pruning, Distillation und jeden weiteren Hebel, um sie auf Embedded-Accelerator-Plattformen (z. B. NVIDIA Jetson, Qualcomm IQ-Serie) schnell und ressourcenschonend zu machen.

- Verantwortung für die Toolchain: Du arbeitest entlang der gesamten Kette aus Model Export, Inference-Optimierungs-Frameworks (z. B. ONNX, TensorRT, AIMET) und den SDKs, die aus einem Modell ein funktionierendes Roboterverhalten machen.

- Optimierung bis auf die Hardware-Ebene: Du profilierst, analysierst und optimierst Modelle und Runtimes, damit sie die Latenz-, Energie- und Speicherbudgets jeder Zielhardware einhalten.

- Zusammenarbeit über Funktionen hinweg: Du arbeitest eng mit Research, Hardware, Software und Product zusammen, um On-Device-AI von der Forschung bis zum ausgelieferten Produkt zu bringen.

- Qualitätsstandards setzen: Du baust Benchmarking, On-Device-Evaluation und Performance-Regressionstests für alle Hardware-Targets auf und pflegst sie.

- Die schwierigen Probleme lösen: Du analysierst Accuracy-Einbußen nach der Quantization, jagst Latenzspitzen und löst Probleme, die erst auf dem echten Chip sichtbar werden.

WAS DU MITBRINGST

- Starkes akademisches Fundament: Ein Master- oder PhD-Abschluss in Informatik, Elektrotechnik, Embedded Systems oder einem verwandten Fachgebiet.

- Nachgewiesene Erfahrung: Mindestens 3 Jahre Erfahrung im Bereich ML oder Embedded-AI-Engineering, inklusive echtem Produktiv-Deployment auf Embedded-Acceleratoren, nicht nur aus Papers.

- Hardware-Know-how: Praktische Erfahrung mit Embedded-AI-Hardwareplattformen (z. B. NVIDIA Jetson, Qualcomm IQ-Serie oder vergleichbar).

- Optimierungs-Expertise: Fundiertes Wissen in der Modelloptimierung von der Modellseite bis zur Hardware: Quantization, Pruning, Distillation und Architecture Search.

- Toolchain-Expertise: Sicherer Umgang mit gängigen Toolchains für Modelloptimierung und Deployment (z. B. ONNX, TensorRT, AIMET oder vergleichbare Hersteller-SDKs).

- Technische Tiefe: Starke Kenntnisse in Python und C++, Erfahrung mit PyTorch sowie Sicherheit im Umgang mit Embedded Linux und Low-Level-Profiling.

- Die richtige Denkweise: Die Überzeugung, dass der einzige echte Test der Ziel-Chip ist und nicht der Trainings-Cluster.

- Zusammenarbeit & Kommunikation: Du arbeitest selbstständig, triffst fundierte Entscheidungen auch bei Unsicherheit und kommunizierst auf Augenhöhe mit Researchern, Hardware-Engineers und Product. Fließendes Englisch ist Voraussetzung, Deutschkenntnisse sind ein starkes Plus (B2 bis C1).

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